Upload folder using huggingface_hub

config.json

@@ -4,6 +4,5 @@
     "AutoConfig": "configuration_sm_subgroup_classifier.SmSubgroupClassifierConfig",
     "AutoModel": "modeling_sm_subgroup_classifier.SmSubgroupClassifier"
   },
-  "available_models": ["fi_HI-NA-nb", "fi_HI-NA-nb-re", "fi_ID", "fi_ID-NA", "fi_NA-nb", "fi_NA-nb-OP", "fi_NA-nb-OP-rv"],
   "model_type": "sm_subgroup_classifier"
 }

en_ID/metadata.pkl

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en_ID/model.pkl

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en_ID/scaler.pkl

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en_ID/training_details.txt

@@ -0,0 +1,39 @@
+Training Details for en_ID
+========================================
+
+Language: en
+Register: ID
+Training Date: 2025-09-26 14:09:04
+
+Data Summary:
+- Total samples: 41071
+- Training samples: 32856
+- Test samples: 8215
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 2
+- Class names: 'sports', ''
+- Class distribution: {'sports': 983, '': 40088}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.9984783931832014, 0.9987825292953888, 0.9975650585907777, 0.9989347131334653, 0.9981737939430833]
+- CV mean: 0.9984
+- CV std: 0.0005
+- CV confidence interval: 0.9984 ± 0.0010
+
+Final Performance:
+- Test accuracy: 0.9988
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

en_NA-nb-OP/metadata.pkl

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en_NA-nb-OP/model.pkl

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en_NA-nb-OP/scaler.pkl

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en_NA-nb-OP/training_details.txt

@@ -0,0 +1,39 @@
+Training Details for en_NA-nb-OP
+========================================
+
+Language: en
+Register: NA-nb-OP
+Training Date: 2025-09-26 14:08:46
+
+Data Summary:
+- Total samples: 1536
+- Training samples: 1228
+- Test samples: 308
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 4
+- Class names: '', 'culture', 'dining', 'lifestyle'
+- Class distribution: {'': 747, 'culture': 327, 'dining': 172, 'lifestyle': 290}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.9634146341463414, 0.959349593495935, 0.9634146341463414, 0.9877551020408163, 0.9755102040816327]
+- CV mean: 0.9699
+- CV std: 0.0104
+- CV confidence interval: 0.9699 ± 0.0209
+
+Final Performance:
+- Test accuracy: 0.9643
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

en_NA-nb/metadata.pkl

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en_NA-nb/model.pkl

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en_NA-nb/scaler.pkl

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en_NA-nb/training_details.txt

@@ -0,0 +1,39 @@
+Training Details for en_NA-nb
+========================================
+
+Language: en
+Register: NA-nb
+Training Date: 2025-09-26 14:08:58
+
+Data Summary:
+- Total samples: 49026
+- Training samples: 39220
+- Test samples: 9806
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 2
+- Class names: '', 'comments'
+- Class distribution: {'': 46239, 'comments': 2787}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.989673635900051, 0.9886537480877103, 0.9903110657827638, 0.9899286078531362, 0.9903110657827638]
+- CV mean: 0.9898
+- CV std: 0.0006
+- CV confidence interval: 0.9898 ± 0.0012
+
+Final Performance:
+- Test accuracy: 0.9897
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

fi_ID-NA/metadata.pkl

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fi_ID-NA/model.pkl

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fi_ID-NA/scaler.pkl

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fi_ID-NA/training_details.txt

@@ -2,33 +2,29 @@ Training Details for fi_ID-NA
 ========================================
 
 Language: fi
-Model Name: ID-NA
-Training Date: 2025-09-19 09:31:09
+Register: ID-NA
+Training Date: 2025-09-26 14:06:57
 
 Data Summary:
-- Total samples: 1663
-- Training samples: 1330
-- Test samples: 333
+- Total samples: 1729
+- Training samples: 1383
+- Test samples: 346
 - Embedding dimension: 1024
 
 Classes:
 - Number of classes: 2
-- Class names: blog_comments, general
-- Class distribution: {'blog_comments': 1317, 'general': 346}
-
-Cluster Merging:
-- 'blog_comments': kept cluster 1 (no merge)
-- 'general': kept cluster 2 (no merge)
+- Class names: '', 'comments'
+- Class distribution: {'': 389, 'comments': 1340}
 
 Cross-Validation Results:
 - CV folds: 5
-- CV scores: [0.9849624060150376, 0.9887218045112782, 0.9849624060150376, 0.9924812030075187, 0.981203007518797]
-- CV mean: 0.9865
-- CV std: 0.0038
-- CV confidence interval: 0.9865 ± 0.0077
+- CV scores: [0.9855595667870036, 0.9927797833935018, 0.9927797833935018, 0.9927536231884058, 0.9818840579710145]
+- CV mean: 0.9892
+- CV std: 0.0046
+- CV confidence interval: 0.9892 ± 0.0092
 
 Final Performance:
-- Test accuracy: 0.9940
+- Test accuracy: 0.9913
 
 Model Configuration:
 - Algorithm: Logistic Regression

fi_ID/metadata.pkl

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fi_ID/model.pkl

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fi_ID/scaler.pkl

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fi_ID/training_details.txt

@@ -2,8 +2,8 @@ Training Details for fi_ID
 ========================================
 
 Language: fi
-Model Name: ID
-Training Date: 2025-09-19 09:20:03
+Register: ID
+Training Date: 2025-09-26 14:08:45
 
 Data Summary:
 - Total samples: 34973
@@ -13,22 +13,18 @@ Data Summary:
 
 Classes:
 - Number of classes: 2
-- Class names: general, blog_comments
-- Class distribution: {'general': 32370, 'blog_comments': 2603}
-
-Cluster Merging:
-- 'general': merged clusters [np.int64(0), np.int64(2), np.int64(3)]
-- 'blog_comments': kept cluster 1 (no merge)
+- Class names: '', 'sports'
+- Class distribution: {'': 34495, 'sports': 478}
 
 Cross-Validation Results:
 - CV folds: 5
-- CV scores: [0.9960686204431737, 0.9958899213724088, 0.9974982130092923, 0.9935656836461126, 0.9951742627345844]
-- CV mean: 0.9956
-- CV std: 0.0013
-- CV confidence interval: 0.9956 ± 0.0026
+- CV scores: [0.9980343102215868, 0.9987491065046462, 0.9992852037169406, 0.9987488829311886, 0.9978552278820375]
+- CV mean: 0.9985
+- CV std: 0.0005
+- CV confidence interval: 0.9985 ± 0.0010
 
 Final Performance:
-- Test accuracy: 0.9963
+- Test accuracy: 0.9986
 
 Model Configuration:
 - Algorithm: Logistic Regression

fi_NA-nb-OP-rv/metadata.pkl

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fi_NA-nb-OP-rv/model.pkl

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fi_NA-nb-OP-rv/scaler.pkl

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fi_NA-nb-OP-rv/training_details.txt

@@ -2,8 +2,8 @@ Training Details for fi_NA-nb-OP-rv
 ========================================
 
 Language: fi
-Model Name: NA-nb-OP-rv
-Training Date: 2025-09-18 16:24:05
+Register: NA-nb-OP-rv
+Training Date: 2025-09-26 14:06:58
 
 Data Summary:
 - Total samples: 4929
@@ -13,18 +13,18 @@ Data Summary:
 
 Classes:
 - Number of classes: 4
-- Class names: literature, dining, beer, cosmetics
-- Class distribution: {'literature': 2958, 'dining': 296, 'beer': 282, 'cosmetics': 1393}
+- Class names: 'books', 'dining', 'beverages', 'cosmetics'
+- Class distribution: {'books': 2958, 'dining': 294, 'beverages': 283, 'cosmetics': 1394}
 
 Cross-Validation Results:
 - CV folds: 5
-- CV scores: [0.9898605830164765, 0.9885931558935361, 0.991128010139417, 0.9923857868020305, 0.9911167512690355]
-- CV mean: 0.9906
-- CV std: 0.0013
-- CV confidence interval: 0.9906 ± 0.0026
+- CV scores: [0.9898605830164765, 0.9936628643852978, 0.9936628643852978, 0.9885786802030457, 0.9911167512690355]
+- CV mean: 0.9914
+- CV std: 0.0020
+- CV confidence interval: 0.9914 ± 0.0041
 
 Final Performance:
-- Test accuracy: 0.9939
+- Test accuracy: 0.9899
 
 Model Configuration:
 - Algorithm: Logistic Regression

fi_NA-nb-OP/metadata.pkl

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fi_NA-nb-OP/model.pkl

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fi_NA-nb-OP/scaler.pkl

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fi_NA-nb-OP/training_details.txt

@@ -2,8 +2,8 @@ Training Details for fi_NA-nb-OP
 ========================================
 
 Language: fi
-Model Name: NA-nb-OP
-Training Date: 2025-09-18 16:21:06
+Register: NA-nb-OP
+Training Date: 2025-09-26 14:06:57
 
 Data Summary:
 - Total samples: 1969
@@ -13,18 +13,18 @@ Data Summary:
 
 Classes:
 - Number of classes: 3
-- Class names: general, consumption, culture
-- Class distribution: {'general': 874, 'consumption': 389, 'culture': 706}
+- Class names: '', 'culture', 'consumption'
+- Class distribution: {'': 874, 'culture': 695, 'consumption': 400}
 
 Cross-Validation Results:
 - CV folds: 5
-- CV scores: [0.9777777777777777, 0.9841269841269841, 0.9714285714285714, 0.9809523809523809, 0.9904761904761905]
+- CV scores: [0.9841269841269841, 0.9936507936507937, 0.9841269841269841, 0.9777777777777777, 0.9650793650793651]
 - CV mean: 0.9810
-- CV std: 0.0063
-- CV confidence interval: 0.9810 ± 0.0127
+- CV std: 0.0094
+- CV confidence interval: 0.9810 ± 0.0188
 
 Final Performance:
-- Test accuracy: 0.9772
+- Test accuracy: 0.9848
 
 Model Configuration:
 - Algorithm: Logistic Regression

fi_NA-nb/metadata.pkl

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fi_NA-nb/model.pkl

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fi_NA-nb/scaler.pkl

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fi_NA-nb/training_details.txt

@@ -2,8 +2,8 @@ Training Details for fi_NA-nb
 ========================================
 
 Language: fi
-Model Name: NA-nb
-Training Date: 2025-09-19 09:35:23
+Register: NA-nb
+Training Date: 2025-09-26 14:08:40
 
 Data Summary:
 - Total samples: 218088
@@ -13,22 +13,18 @@ Data Summary:
 
 Classes:
 - Number of classes: 2
-- Class names: blog_comments, general
-- Class distribution: {'blog_comments': 24862, 'general': 193226}
-
-Cluster Merging:
-- 'blog_comments': kept cluster 1 (no merge)
-- 'general': kept cluster 2 (no merge)
+- Class names: '', 'comments'
+- Class distribution: {'': 193558, 'comments': 24530}
 
 Cross-Validation Results:
 - CV folds: 5
-- CV scores: [0.9941823809250874, 0.9942396973691752, 0.9939817733707801, 0.9939817733707801, 0.9939817733707801]
-- CV mean: 0.9941
-- CV std: 0.0001
-- CV confidence interval: 0.9941 ± 0.0002
+- CV scores: [0.9926061787126726, 0.9932939760417264, 0.9922622800481458, 0.9933512924858142, 0.992778128044936]
+- CV mean: 0.9929
+- CV std: 0.0004
+- CV confidence interval: 0.9929 ± 0.0008
 
 Final Performance:
-- Test accuracy: 0.9945
+- Test accuracy: 0.9927
 
 Model Configuration:
 - Algorithm: Logistic Regression

modeling_sm_subgroup_classifier.py

@@ -2,7 +2,6 @@ import os
 import pickle
 
 import joblib
-import numpy as np
 import torch
 from transformers import PreTrainedModel
 
@@ -17,27 +16,51 @@ class SmSubgroupClassifier(PreTrainedModel):
         self.config = config
         self._loaded_classifiers = {}
         self.model_dir = None
+        self._available_models = None
+
+    @property
+    def available_models(self):
+        """Auto-discover available models"""
+        if self._available_models is None:
+            self._available_models = self._discover_available_models()
+        return self._available_models
+
+    def _discover_available_models(self):
+        """Scan model directory for available models"""
+        if not self.model_dir or not os.path.exists(self.model_dir):
+            return []
+
+        models = []
+        for item in os.listdir(self.model_dir):
+            item_path = os.path.join(self.model_dir, item)
+            if os.path.isdir(item_path):
+                # Verify it's a valid model directory
+                required_files = ["model.pkl", "scaler.pkl", "metadata.pkl"]
+                if all(
+                    os.path.exists(os.path.join(item_path, f)) for f in required_files
+                ):
+                    models.append(item)
+
+        return sorted(models)
 
     def _load_classifier(self, model_key):
         """Load a specific classifier by model key (e.g., 'en_OP-ob')"""
         if model_key in self._loaded_classifiers:
             return self._loaded_classifiers[model_key]
 
-        if model_key not in self.config.available_models:
+        if model_key not in self.available_models:
             raise ValueError(
-                f"Model '{model_key}' not available. Available: {self.config.available_models}"
+                f"Model '{model_key}' not available. Available: {self.available_models}"
             )
 
         # Path to classifier
         classifier_path = os.path.join(self.model_dir, model_key)
-
         if not os.path.exists(classifier_path):
             raise FileNotFoundError(f"Classifier not found at {classifier_path}")
 
         # Load components
         classifier = joblib.load(os.path.join(classifier_path, "model.pkl"))
         scaler = joblib.load(os.path.join(classifier_path, "scaler.pkl"))
-
         with open(os.path.join(classifier_path, "metadata.pkl"), "rb") as f:
             metadata = pickle.load(f)
 
@@ -63,7 +86,6 @@ class SmSubgroupClassifier(PreTrainedModel):
         # Convert embeddings to numpy if needed
         if torch.is_tensor(embeddings):
             embeddings = embeddings.detach().cpu().numpy()
-
         if embeddings.ndim == 1:
             embeddings = embeddings.reshape(1, -1)
 
@@ -79,13 +101,11 @@ class SmSubgroupClassifier(PreTrainedModel):
         results = []
         for pred, probs in zip(predictions, probabilities):
             predicted_class_name = classifier_info["class_names"][pred]
-
             # Get all class probabilities
             all_probs = {
                 classifier_info["class_names"][i]: float(prob)
                 for i, prob in enumerate(probs)
             }
-
             results.append(
                 {
                     "label": predicted_class_name,

sv_HI-NA-nb-re/metadata.pkl

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sv_HI-NA-nb-re/model.pkl

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sv_HI-NA-nb-re/scaler.pkl

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sv_HI-NA-nb-re/training_details.txt

@@ -0,0 +1,39 @@
+Training Details for sv_HI-NA-nb-re
+========================================
+
+Language: sv
+Register: HI-NA-nb-re
+Training Date: 2025-09-26 14:05:16
+
+Data Summary:
+- Total samples: 6804
+- Training samples: 5443
+- Test samples: 1361
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 2
+- Class names: 'crafts', ''
+- Class distribution: {'crafts': 135, '': 6669}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.9963269054178145, 0.9963269054178145, 0.9972451790633609, 0.9944852941176471, 0.9954044117647058]
+- CV mean: 0.9960
+- CV std: 0.0009
+- CV confidence interval: 0.9960 ± 0.0019
+
+Final Performance:
+- Test accuracy: 0.9956
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

sv_ID-NA-nb/metadata.pkl

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sv_ID-NA-nb/training_details.txt

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+Training Details for sv_ID-NA-nb
+========================================
+
+Language: sv
+Register: ID-NA-nb
+Training Date: 2025-09-26 14:05:20
+
+Data Summary:
+- Total samples: 1189
+- Training samples: 951
+- Test samples: 238
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 2
+- Class names: '', 'comments'
+- Class distribution: {'': 852, 'comments': 337}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.9214659685863874, 0.9526315789473684, 0.9315789473684211, 0.968421052631579, 0.9263157894736842]
+- CV mean: 0.9401
+- CV std: 0.0177
+- CV confidence interval: 0.9401 ± 0.0354
+
+Final Performance:
+- Test accuracy: 0.9706
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

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sv_ID/training_details.txt

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+Training Details for sv_ID
+========================================
+
+Language: sv
+Register: ID
+Training Date: 2025-09-26 14:06:57
+
+Data Summary:
+- Total samples: 15060
+- Training samples: 12048
+- Test samples: 3012
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 3
+- Class names: '', 'sports', 'help'
+- Class distribution: {'': 14108, 'sports': 542, 'help': 410}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.9962655601659751, 0.9954356846473029, 0.9983402489626556, 1.0, 0.9991697799916978]
+- CV mean: 0.9978
+- CV std: 0.0017
+- CV confidence interval: 0.9978 ± 0.0035
+
+Final Performance:
+- Test accuracy: 0.9977
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt

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sv_IN-NA-nb/training_details.txt

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+Training Details for sv_IN-NA-nb
+========================================
+
+Language: sv
+Register: IN-NA-nb
+Training Date: 2025-09-26 14:05:18
+
+Data Summary:
+- Total samples: 1169
+- Training samples: 935
+- Test samples: 234
+- Embedding dimension: 1024
+
+Classes:
+- Number of classes: 2
+- Class names: '', 'organizations'
+- Class distribution: {'': 811, 'organizations': 358}
+
+Cross-Validation Results:
+- CV folds: 5
+- CV scores: [0.93048128342246, 0.9893048128342246, 0.9625668449197861, 0.9679144385026738, 0.9625668449197861]
+- CV mean: 0.9626
+- CV std: 0.0188
+- CV confidence interval: 0.9626 ± 0.0377
+
+Final Performance:
+- Test accuracy: 0.9744
+
+Model Configuration:
+- Algorithm: Logistic Regression
+- Regularization (C): 1.0
+- Feature scaling: StandardScaler
+- Random state: 42
+
+Files:
+- Classifier: model.pkl
+- Scaler: scaler.pkl
+- Metadata: metadata.pkl
+- This file: training_details.txt