From a5f0c254c0dcd4f15a8243be227e5affea7cff7d Mon Sep 17 00:00:00 2001
From: Ismail Ashraq <issey1455@gmail.com>
Date: Wed, 21 Feb 2024 13:54:38 +0500
Subject: [PATCH] set default batch size and update progress bar description
---
semantic_router/layer.py | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index e7381df0..694f4ebe 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -110,9 +110,9 @@ class LayerConfig:
llm_class = getattr(llm_module, llm_data["class"])
# Instantiate the LLM class with the provided model name
llm = llm_class(name=llm_data["model"])
- route_data[
- "llm"
- ] = llm # Reassign the instantiated llm object back to route_data
+ route_data["llm"] = (
+ llm # Reassign the instantiated llm object back to route_data
+ )
# Dynamically create the Route object using the remaining route_data
route = Route(**route_data)
@@ -431,19 +431,19 @@ class RouteLayer:
self,
X: List[str],
y: List[str],
- batch_size: int,
+ batch_size: int = 500,
max_iter: int = 500,
):
# convert inputs into array
Xq: List[List[float]] = []
- for i in tqdm(range(0, len(X), batch_size), desc="Processing batches"):
+ for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
emb = np.array(self.encoder(X[i : i + batch_size]))
Xq.extend(emb)
# initial eval (we will iterate from here)
best_acc = self._vec_evaluate(Xq=np.array(Xq), y=y)
best_thresholds = self.get_thresholds()
# begin fit
- for _ in (pbar := tqdm(range(max_iter))):
+ for _ in (pbar := tqdm(range(max_iter), desc="Training")):
pbar.set_postfix({"acc": round(best_acc, 2)})
# Find the best score threshold for each route
thresholds = threshold_random_search(
@@ -461,12 +461,12 @@ class RouteLayer:
# update route layer to best thresholds
self._update_thresholds(score_thresholds=best_thresholds)
- def evaluate(self, X: List[str], y: List[str], batch_size: int) -> float:
+ def evaluate(self, X: List[str], y: List[str], batch_size: int = 500) -> float:
"""
Evaluate the accuracy of the route selection.
"""
Xq: List[List[float]] = []
- for i in tqdm(range(0, len(X), batch_size), desc="Processing batches"):
+ for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
emb = np.array(self.encoder(X[i : i + batch_size]))
Xq.extend(emb)
--
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