Update Hebrew Intent Model - Enhanced with data augmentation (135 examples)

README.md

@@ -1,199 +1,141 @@
 ---
-library_name: transformers
-tags: []
+language: he
+license: apache-2.0
+datasets:
+- custom
+tags:
+- text-classification
+- intent-classification
+- hebrew
+- nlp
+- bert
+- customer-service
+widget:
+- text: "שכחתי את הסיסמה שלי"
+  example_title: "Password Reset"
+- text: "רוצה לבטל את המנוי"
+  example_title: "Cancel Subscription"
+- text: "כמה עולה החבילה"
+  example_title: "General Question"
+- text: "האתר לא עובד"
+  example_title: "Technical Support"
 ---
 
-# Model Card for Model ID
+# Hebrew Intent Classification Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned BERT model for Hebrew intent classification, specifically designed for customer service scenarios. It can classify Hebrew text into 4 different intent categories commonly found in customer support interactions.
 
+## Supported Intent Classes
 
-## Model Details
+1. **ביטול מנוי** (Cancel Subscription) - Requests to cancel or terminate services
+2. **שאלה כללית** (General Question) - General inquiries about services, pricing, or account management  
+3. **שכחת סיסמה** (Password Reset) - Issues related to forgotten passwords or login problems
+4. **תמיכה טכנית** (Technical Support) - Technical issues, bugs, or system problems
 
-### Model Description
+## Usage
 
-<!-- Provide a longer summary of what this model is. -->
+```python
+from transformers import pipeline
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+# Load the model
+classifier = pipeline("text-classification", model="Huggingm1r@n/hebrew-intent-classifier")
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+# Make predictions
+result = classifier("שכחתי את הסיסמה שלי")
+print(result)
+# [{'label': 'שכחת סיסמה', 'score': 0.95}]
 
-### Model Sources [optional]
+# Test other examples
+examples = [
+    "רוצה לבטל את המנוי",
+    "כמה עולה החבילה", 
+    "האתר לא עובד"
+]
 
-<!-- Provide the basic links for the model. -->
+for text in examples:
+    result = classifier(text)
+    print(f"'{text}' -> {result[0]['label']} ({result[0]['score']:.2%})")
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Direct Usage with Transformers
 
-## Uses
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Huggingm1r@n/hebrew-intent-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("Huggingm1r@n/hebrew-intent-classifier")
 
-### Direct Use
+def predict_intent(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=-1)
+    
+    predicted_id = torch.argmax(logits, dim=-1).item()
+    predicted_label = model.config.id2label[predicted_id]
+    confidence = probabilities[0][predicted_id].item()
+    
+    return predicted_label, confidence
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+# Example
+intent, confidence = predict_intent("שכחתי את הסיסמה")
+print(f"Intent: {intent}, Confidence: {confidence:.2%}")
+```
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
+- **Base Model**: bert-base-multilingual-cased
+- **Training Data**: 135 Hebrew customer service examples (augmented from 12 original)
+- **Data Augmentation**: Manual variations, formal/informal styles, polite forms
+- **Performance**: >90% accuracy on validation set
 
-[More Information Needed]
+## Example Predictions
 
-**APA:**
+| Hebrew Text | Predicted Intent | English Translation |
+|------------|------------------|-------------------|
+| שכחתי את הסיסמה שלי | שכחת סיסמה | I forgot my password |
+| רוצה לבטל את המנוי | ביטול מנוי | Want to cancel subscription |
+| כמה עולה החבילה | שאלה כללית | How much does the package cost |
+| האתר לא עובד | תמיכה טכנית | The website doesn't work |
 
-[More Information Needed]
+## Use Cases
 
-## Glossary [optional]
+- **Customer Service Chatbots**: Route Hebrew customer queries automatically
+- **Support Ticket Classification**: Categorize support requests by intent
+- **Voice of Customer Analysis**: Analyze Hebrew customer feedback
+- **Automated Response Systems**: Trigger appropriate responses based on intent
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Limitations
 
-[More Information Needed]
+- Designed for customer service domain specifically
+- Limited to 4 predefined intent classes
+- May not work well with very informal Hebrew or slang
+- Requires Hebrew text input
 
-## More Information [optional]
+## Model Files
 
-[More Information Needed]
+- Uses `safetensors` format for secure model storage
+- Compatible with latest Transformers library
+- Includes comprehensive tokenizer configuration
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{hebrew-intent-classifier-2025,
+  title={Hebrew Intent Classification Model for Customer Service},
+  author={Huggingm1r@n},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Huggingm1r@n/hebrew-intent-classifier}
+}
+```
 
-## Model Card Contact
+## License
 
-[More Information Needed]
+This model is released under the Apache 2.0 License.

config.json

@@ -4,27 +4,23 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
-  "gradient_checkpointing": false,
+  "directionality": "bidi",
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5"
+    "0": "\u05d1\u05d9\u05d8\u05d5\u05dc \u05de\u05e0\u05d5\u05d9",
+    "1": "\u05e9\u05d0\u05dc\u05d4 \u05db\u05dc\u05dc\u05d9\u05ea",
+    "2": "\u05e9\u05db\u05d7\u05ea \u05e1\u05d9\u05e1\u05de\u05d4",
+    "3": "\u05ea\u05de\u05d9\u05db\u05d4 \u05d8\u05db\u05e0\u05d9\u05ea"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4,
-    "LABEL_5": 5
+    "\u05d1\u05d9\u05d8\u05d5\u05dc \u05de\u05e0\u05d5\u05d9": 0,
+    "\u05e9\u05d0\u05dc\u05d4 \u05db\u05dc\u05dc\u05d9\u05ea": 1,
+    "\u05e9\u05db\u05d7\u05ea \u05e1\u05d9\u05e1\u05de\u05d4": 2,
+    "\u05ea\u05de\u05d9\u05db\u05d4 \u05d8\u05db\u05e0\u05d9\u05ea": 3
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
@@ -32,11 +28,16 @@
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
   "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "total_flos": 6997313242916978688,
   "transformers_version": "4.55.0",
   "type_vocab_size": 2,
   "use_cache": true,
-  "vocab_size": 30522
+  "vocab_size": 119547
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df68e41ee45a046376fa0f4bdb1026290747d5e9f1b87f9a96f252e73c751a8f
-size 437970952
+oid sha256:63c09aefdfac2af07fab6891ac4a15b7e301300cf5732451bc6620d5401abbdf
+size 711449608

tokenizer_config.json

@@ -8,7 +8,7 @@
       "single_word": false,
       "special": true
     },
-    "1": {
+    "100": {
       "content": "[UNK]",
       "lstrip": false,
       "normalized": false,
@@ -16,7 +16,7 @@
       "single_word": false,
       "special": true
     },
-    "2": {
+    "101": {
       "content": "[CLS]",
       "lstrip": false,
       "normalized": false,
@@ -24,7 +24,7 @@
       "single_word": false,
       "special": true
     },
-    "3": {
+    "102": {
       "content": "[SEP]",
       "lstrip": false,
       "normalized": false,
@@ -32,7 +32,7 @@
       "single_word": false,
       "special": true
     },
-    "4": {
+    "103": {
       "content": "[MASK]",
       "lstrip": false,
       "normalized": false,
@@ -41,14 +41,12 @@
       "special": true
     }
   },
-  "clean_up_tokenization_spaces": true,
+  "clean_up_tokenization_spaces": false,
   "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
+  "do_lower_case": false,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
-  "model_max_length": 1000000000000000019884624838656,
-  "never_split": null,
+  "model_max_length": 512,
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "strip_accents": null,