Hugging Face's logo

FinisYazilim
/
product-classifier-v1

Text Classification
PyTorch
Safetensors
product-classification
cnn-bilstm
turkish
Model card Files
xet
Community
turgutguvercin commited on
Commit
234483c
·
verified ·
1 Parent(s): 3b71641

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Files changed (6) hide show
  1. .gitattributes +1 -0
  2. README.md +53 -0
  3. config.json +16 -0
  4. label_mappings.json +4461 -0
  5. model.safetensors +3 -0
  6. tokenizer.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ library_name: pytorch
4
+ tags:
5
+ - product-classification
6
+ - cnn-bilstm
7
+ - text-classification
8
+ - turkish
9
+ pipeline_tag: text-classification
10
+ ---
11
+
12
+ # CNN + BiLSTM Product Classification Model
13
+
14
+ This model uses a hybrid CNN + BiLSTM architecture for product classification.
15
+
16
+ ## Model Details
17
+ - **Architecture**: Multi-kernel CNN + Bidirectional LSTM with attention
18
+ - **Embedding Dimension**: 512
19
+ - **Vocabulary Size**: 33,961
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+ - **Number of Classes**: 2,227
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+ - **Best Validation Accuracy**: 0.7105
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+
23
+ ## Features
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+ - Multi-kernel CNN with kernel sizes: [2, 3, 4, 5]
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+ - Bidirectional LSTM with soft attention mechanism
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+ - Ad-hoc features (title length, character statistics, etc.)
27
+ - Word2Vec embeddings trained on product titles
28
+
29
+ ## Usage
30
+
31
+ ```python
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+ from model_inference import CNNBiLSTMInference
33
+
34
+ # Load the model
35
+ inference = CNNBiLSTMInference.from_pretrained("your-username/model-name")
36
+
37
+ # Predict categories
38
+ texts = [
39
+ "Yataş Bedding BAMBU Yorgan",
40
+ "Arji Ev ve Ofis Çalışma Sandalyesi"
41
+ ]
42
+
43
+ predictions = inference.predict(texts, top_k=3)
44
+ for text, pred in zip(texts, predictions):
45
+ print(f"Text: {text}")
46
+ for label, score in pred:
47
+ print(f" → {label}: {score:.4f}")
48
+ ```
49
+
50
+ ## Training Details
51
+ - **Epochs**: 20
52
+ - **Batch Size**: 128
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+ - **Learning Rate**: 0.001
config.json ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "vocab_size": 33961,
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+ "num_classes": 2227,
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+ "embedding_dim": 512,
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+ "cnn_filters": 128,
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+ "lstm_hidden": 256,
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+ "max_length": 50,
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+ "adhoc_features_dim": 15,
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+ "kernel_sizes": [
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+ 2,
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+ 3,
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+ 4,
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+ 5
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+ ],
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+ "best_val_acc": 0.7105317608110904
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+ }
label_mappings.json ADDED
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