diff --git a/aicrowd.json b/aicrowd.json
index 3fe7d2e3e00e8123d64fd5bf61ef47bd8bf28ece..71fd837392a7975c5405958243c702f869e521b5 100755
--- a/aicrowd.json
+++ b/aicrowd.json
@@ -1,8 +1,8 @@
{
"challenge_id": "esci-challenge-for-improving-product-search",
- "task": "task_1_query-product_ranking",
+ "task": "task_2_multiclass_product_classification",
"gpu": true,
"authors": ["aicrowd-username"],
- "description": "task_3_product_substitute_identification, task_2_multiclass_product_classification",
+ "description": "task_3_product_substitute_identification, task_1_query-product_ranking",
"license": "MIT"
}
\ No newline at end of file
diff --git a/utils/lgb_predict_task2.py b/utils/lgb_predict_task2.py
index ad66fcf95f62e9f14c459f39053072aec2867822..c6f325981ddd688986b8a1b1cc0f26ceb42673a3 100755
--- a/utils/lgb_predict_task2.py
+++ b/utils/lgb_predict_task2.py
@@ -10,7 +10,7 @@ LGB_CONFIG = {
"model_file": "/models/lgb-us-task-2.txt",
"features": ['pred_0','pred_1','pred_2','pred_3',
'label_0','label_1','label_2','label_3',
- 'query_count','is_isbn','has_isbn','fold']
+ 'query_count','is_isbn','has_isbn', 'fold', 'model']
},
"jp": {
"product_feat": "/models/es-jp-product-feat-remove-intersection.csv",
@@ -48,11 +48,10 @@ def lgb_predict_task2(df, locale):
)
temp = (
df.groupby("query")["example_id"]
- .count()
+ .nunique()
.reset_index()
.rename({"example_id": "query_count"}, axis=1)
)
- temp["query_count"] = temp["query_count"] // df["fold"].nunique()
df = pd.merge(left=df, right=temp, on="query", how="left") # query_count
df["is_isbn"] = df["product_id"].apply(lambda x: int(x[0] != "B")) # is_isbn
temp = (
diff --git a/utils/lgb_predict_task3.py b/utils/lgb_predict_task3.py
index 26e79b3de8ce3f2ab899d9156d782cd21adf11ae..f972a4b3097bd79002555e3ec65dcc871e3d48b4 100755
--- a/utils/lgb_predict_task3.py
+++ b/utils/lgb_predict_task3.py
@@ -7,7 +7,7 @@ LGB_CONFIG = {
"model_file": "/models/lgb-us-task-3.txt",
"features": ['pred_0','pred_1','pred_2','pred_3',
'label_0','label_1','label_2','label_3',
- 'query_count','is_isbn','has_isbn','fold']
+ 'query_count','is_isbn','has_isbn', 'fold', 'model']
},
"jp": {
"product_feat": "/models/es-jp-product-feat-remove-intersection.csv",
@@ -46,11 +46,10 @@ def lgb_predict_task3(df, locale):
)
temp = (
df.groupby("query")["example_id"]
- .count()
+ .nunique()
.reset_index()
.rename({"example_id": "query_count"}, axis=1)
)
- temp["query_count"] = temp["query_count"] // df["fold"].nunique()
df = pd.merge(left=df, right=temp, on="query", how="left") # query_count
df["is_isbn"] = df["product_id"].apply(lambda x: int(x[0] != "B")) # is_isbn
temp = (
diff --git a/utils/run_task2.py b/utils/run_task2.py
index 3956f073a33caa4d2bf673e7fdb0826bc800184d..85001b2274b704c75960e7138900f638f869b48d 100755
--- a/utils/run_task2.py
+++ b/utils/run_task2.py
@@ -5,7 +5,6 @@ from .onnx_predict import onnx_predict
from .lgb_predict_task2 import lgb_predict_task2
from scipy.special import softmax
-# ensemble 4 models for task 2
CONFIG = {
"us": [
{
@@ -59,7 +58,7 @@ CONFIG = {
"product_brand": 4609,
"product_color_name": 3225,
},
- "model": ["/models/us-bigbird-des-0-fp16.onnx", "/models/us-bigbird-des-1-fp16.onnx"],
+ "model": ["/models/us-bigbird-kd-0-fp16.onnx", "/models/us-bigbird-kd-1-fp16.onnx"],
"product": "/models/bigbird.h5",
"key": "us",
"type": "bigbird",
@@ -82,12 +81,12 @@ CONFIG = {
"product_color_name": 2551,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "jp",
"type": "mdeberta",
"fold_offset": 0,
- "description":False,
+ "description":True,
},
],
"es": [
@@ -104,12 +103,12 @@ CONFIG = {
"product_color_name": 6776,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "es",
"type": "mdeberta",
"fold_offset": 0,
- "description": False,
+ "description": True,
},
],
}
@@ -155,6 +154,7 @@ class Task2Predictor(BasePredictor):
pred = onnx_predict(sub_test, config)
pred[list(range(4))] = softmax(pred[list(range(4))].values, 1) # type: ignore
pred.columns = ["example_id", "fold"] + ["pred_0",'pred_1','pred_2','pred_3']
+ pred['model'] = i
onnx_pred.append(pred.copy())
progress += 0.1
register_progress(progress)
diff --git a/utils/run_task3.py b/utils/run_task3.py
index f5bf0b4fd0ae484569ecc26a18499a72f87d6b54..3d23762c35816a57a6e4bb69ba46133dbaa54c22 100755
--- a/utils/run_task3.py
+++ b/utils/run_task3.py
@@ -4,6 +4,7 @@ from .clean import DeBertaCleanV2, ESclean, JSClean # type: ignore
from .onnx_predict import onnx_predict
from .lgb_predict_task3 import lgb_predict_task3
from scipy.special import softmax
+import time
CONFIG = {
"us": [
@@ -58,7 +59,7 @@ CONFIG = {
"product_brand": 4609,
"product_color_name": 3225,
},
- "model": ["/models/us-bigbird-des-0-fp16.onnx", "/models/us-bigbird-des-1-fp16.onnx"],
+ "model": ["/models/us-bigbird-kd-0-fp16.onnx", "/models/us-bigbird-kd-1-fp16.onnx"],
"product": "/models/bigbird.h5",
"key": "us",
"type": "bigbird",
@@ -81,12 +82,12 @@ CONFIG = {
"product_color_name": 2551,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "jp",
"type": "mdeberta",
"fold_offset": 0,
- "description":False,
+ "description":True,
},
],
"es": [
@@ -103,12 +104,12 @@ CONFIG = {
"product_color_name": 6776,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "es",
"type": "mdeberta",
"fold_offset": 0,
- "description": False,
+ "description": True,
},
],
}
@@ -154,6 +155,7 @@ class Task3Predictor(BasePredictor):
pred = onnx_predict(sub_test, config)
pred[list(range(4))] = softmax(pred[list(range(4))].values, 1) # type: ignore
pred.columns = ["example_id", "fold"] + ["pred_0",'pred_1','pred_2','pred_3']
+ pred['model'] = i
onnx_pred.append(pred.copy())
progress += 0.1
register_progress(progress)
@@ -165,4 +167,4 @@ class Task3Predictor(BasePredictor):
predictions_df = pd.concat(all_output).reset_index(drop=True)
print("Writing Task-3 Predictions to : ", predictions_output_path)
predictions_df.to_csv(predictions_output_path, index=False, header=True)
- register_progress(1)
+ register_progress(1)
\ No newline at end of file