Add BPNet model ENCSR168PCB (ENCSR250WFW)

.gitattributes

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README.md

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+---
+license: cc-by-4.0
+library_name: bpnet
+tags:
+  - bpnet
+  - dna
+  - genomics
+  - transcription-factor-binding
+  - encode
+  - ChIP-seq
+  - hg38
+  - qc-unvalidated
+  - RREB1
+---
+
+# ENCODE BPNet -- RREB1 ChIP-seq in K562 (ENCSR250WFW)
+
+Trained BPNet model (ChIP-seq) from the ENCODE project.
+
+- Experiment: [ENCSR250WFW](https://www.encodeproject.org/experiments/ENCSR250WFW/)
+- Model annotation: [ENCSR168PCB](https://www.encodeproject.org/annotations/ENCSR168PCB/)
+- Assembly: hg38 · Target: RREB1 · Biosample: K562
+
+## QC
+- Status: **unvalidated**
+- Notes: Found potential direct motif (counts);
+
+## Files
+5-fold cross-validation. Each `fold_*/` holds the trained model in two forms:
+- `model.h5` — Keras weights (needs the `bpnet` custom layer to load)
+- `saved_model/` — TensorFlow SavedModel (portable; loads with no extra deps)
+
+## Load
+```python
+from huggingface_hub import snapshot_download
+import tensorflow as tf
+d = snapshot_download("kundajelab/encode-bpnet-RREB1-ChIP-seq-K562-ENCSR250WFW-ENCSR168PCB")
+model = tf.saved_model.load(f"{d}/fold_0/saved_model")  # portable
+# Keras .h5 (needs the bpnet package):
+# from bpnet.model.custommodel import CustomModel
+# m = tf.keras.models.load_model(f"{d}/fold_0/model.h5",
+#         custom_objects={'CustomModel': CustomModel})
+```
+
+## Inference inputs
+The `serving_default` signature takes **three** inputs (not sequence alone):
+- `sequence` — one-hot DNA, shape `(N, 2114, 4)`
+- `profile_bias_input_0` — control (bias) profile track, shape `(N, 1000, 2)`
+- `counts_bias_input_0` — control log-count(s), shape `(N, 2)`
+
+The bias inputs are the experiment's matched control signal (the model file's `derived_from` control bigWigs on the ENCODE portal). Outputs: `profile_predictions` `(N, 1000, 2)` and `logcounts_predictions` `(N, 1)`. Reverse-complement averaging is the production default.
+
+## License & citation
+Released under CC-BY-4.0, matching the [ENCODE data-use policy](https://www.encodeproject.org/about/data-use-policy/). Please cite the ENCODE Project Consortium and the model software: [BPNet](https://github.com/kundajelab/bpnet) (Avsec et al., Nat Genet 2021).

config.json

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+{
+    "input_len": 2114,
+    "output_profile_len": 1000,
+    "motif_module_params": {
+        "filters": [64],
+        "kernel_sizes": [21],
+        "padding": "valid"
+    },
+    "syntax_module_params": {
+        "num_dilation_layers": 8,
+        "filters": 64,
+        "kernel_size": 3,
+        "padding": "valid",
+        "pre_activation_residual_unit": true
+    },
+    "profile_head_params": {
+        "filters": 1,
+        "kernel_size":  75,
+        "padding": "valid"
+    },
+    "counts_head_params": {
+        "filters": 1,
+        "kernel_size":  75,
+        "padding": "valid",
+        "units": [1],
+        "activations":["linear"],
+		"dropouts":[0]
+		
+    },
+    "profile_bias_module_params": {
+        "kernel_sizes": [1]
+    },
+    "counts_bias_module_params": {
+    },
+    "use_attribution_prior": false,
+    "attribution_prior_params": {
+        "frequency_limit": 150,
+        "limit_softness": 0.2,
+        "grad_smooth_sigma": 3,
+        "profile_grad_loss_weight": 200,
+        "counts_grad_loss_weight": 100        
+    },
+    "loss_weights": [1, 121.6637090139852],
+    "counts_loss": "MSE"
+}

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