Update README.md

README.md

@@ -32,6 +32,23 @@ Fine-tuned [dicta-il/neodictabert](https://huggingface.co/dicta-il/neodictabert)
 - **Accuracy:** 96.78%
 - **F1 Score:** 96.20%
 
+## Architecture
+
+- **Base Model:** `dicta-il/neodictabert`
+- **Classification Head:** Binary (softmax over 2 classes)
+- **Input Format:** `[CLS] source_article [SEP] summary_claim [SEP]`
+- **Output:** Probability distribution over [contradiction, entailment]
+
+## Training Configuration
+
+- **Learning Rate:** 2e-5
+- **Epochs:** 2
+- **Batch Size:** 2 per device (effective: 16 with gradient accumulation)
+- **Max Sequence Length:** 4,096 tokens
+- **Learning Rate Scheduler:** Linear
+- **Warmup Steps:** 500
+- **Best Model Selection:** Based on eval_f1
+
 ## Usage
 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -40,3 +57,199 @@ import torch
 model_name = "Amit5674/NLI-hebrew-binary-correctness-metric"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
+model.eval()
+
+# Example usage
+article = "ישראל התחילה בהרעשה רגע אחרי הפסקת האש. הממשלה הודיעה על צעדים חדשים..."
+summary = "ישראל התחילה להתרגש רגע אחרי הפסקת האש"
+
+# Tokenize
+inputs = tokenizer(
+    article,
+    summary,
+    return_tensors="pt",
+    padding="max_length",
+    max_length=4096,
+    truncation=True
+)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits[0]
+    probs = torch.softmax(logits, dim=-1)
+    predicted_class_idx = torch.argmax(probs).item()
+    predicted_class = model.config.id2label[predicted_class_idx]
+    confidence = probs[predicted_class_idx].item()
+
+    probabilities = {
+        model.config.id2label[i]: float(probs[i].item())
+        for i in range(model.config.num_labels)
+    }
+
+print(f"Prediction: {predicted_class}")
+print(f"Confidence: {confidence:.4f}")
+print(f"Probabilities: {probabilities}")For detailed inference examples, see the inference scripts and server API documentation.
+
+## Input Format
+
+- **Premise:** Source article text (full document)
+- **Hypothesis:** Summary claim (can be full summary or individual claim)
+- **Processing:** Binary classification (entailment vs contradiction)
+
+## Output Format
+
+- **Prediction:** String label (`"entailment"` or `"contradiction"`)
+- **Confidence:** Probability of predicted class (0.0 to 1.0)
+- **Probabilities:** Dictionary with probabilities for both classes:
+  - `{"entailment": 0.9678, "contradiction": 0.0322}`
+
+## Use Cases
+
+- **Production Fact-Checking:** Fast yes/no contradiction detection for Hebrew summaries
+- **Quality Control:** Automated validation of summary factuality
+- **Batch Processing:** Efficient processing of large document-summary pairs
+- **Real-Time Validation:** Low-latency factuality checking in summary generation pipelines
+
+## Limitations
+
+- Max sequence length: 4,096 tokens (may truncate very long articles)
+- Binary classification: Cannot identify specific error types (use multi-label models for detailed error analysis)
+- Context dependency: Performance may vary with article length and complexity
+- Hebrew-specific: Optimized for Hebrew text; may not generalize to other languages
+
+## Citation
+
+@misc{hebrew_binary_nli_classifier,
+  title={Hebrew Binary NLI Classifier for Factuality Checking},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face}
+}---
+license: apache-2.0
+language:
+- he
+base_model:
+- dicta-il/neodictabert
+tags:
+- nli
+- natural-language-inference
+- hebrew
+- fact-checking
+- contradiction-detection
+pipeline_tag: text-classification
+library_name: transformers
+metrics:
+- accuracy
+- f1
+---
+
+# Hebrew Binary NLI Classifier for Factuality Checking
+
+## Model Description
+
+Fine-tuned [dicta-il/neodictabert](https://huggingface.co/dicta-il/neodictabert) for binary Natural Language Inference in Hebrew. Detects whether a summary claim contradicts a source article.
+
+**Task:** Entailment vs Contradiction Detection  
+**Language:** Hebrew  
+**Max Context:** 4,096 tokens
+
+## Performance
+
+- **Accuracy:** 96.78%
+- **F1 Score:** 96.20%
+
+## Architecture
+
+- **Base Model:** `dicta-il/neodictabert`
+- **Classification Head:** Binary (softmax over 2 classes)
+- **Input Format:** `[CLS] source_article [SEP] summary_claim [SEP]`
+- **Output:** Probability distribution over [contradiction, entailment]
+
+## Training Configuration
+
+- **Learning Rate:** 2e-5
+- **Epochs:** 2
+- **Batch Size:** 2 per device (effective: 16 with gradient accumulation)
+- **Max Sequence Length:** 4,096 tokens
+- **Learning Rate Scheduler:** Linear
+- **Warmup Steps:** 500
+- **Best Model Selection:** Based on eval_f1
+
+## Usage
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "Amit5674/NLI-hebrew-binary-correctness-metric"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
+model.eval()
+
+# Example usage
+article = "ישראל התחילה בהרעשה רגע אחרי הפסקת האש. הממשלה הודיעה על צעדים חדשים..."
+summary = "ישראל התחילה להתרגש רגע אחרי הפסקת האש"
+
+# Tokenize
+inputs = tokenizer(
+    article,
+    summary,
+    return_tensors="pt",
+    padding="max_length",
+    max_length=4096,
+    truncation=True
+)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits[0]
+    probs = torch.softmax(logits, dim=-1)
+    predicted_class_idx = torch.argmax(probs).item()
+    predicted_class = model.config.id2label[predicted_class_idx]
+    confidence = probs[predicted_class_idx].item()
+
+    probabilities = {
+        model.config.id2label[i]: float(probs[i].item())
+        for i in range(model.config.num_labels)
+    }
+
+print(f"Prediction: {predicted_class}")
+print(f"Confidence: {confidence:.4f}")
+print(f"Probabilities: {probabilities}")For detailed inference examples, see the inference scripts and server API documentation.
+
+## Input Format
+
+- **Premise:** Source article text (full document)
+- **Hypothesis:** Summary claim (can be full summary or individual claim)
+- **Processing:** Binary classification (entailment vs contradiction)
+
+## Output Format
+
+- **Prediction:** String label (`"entailment"` or `"contradiction"`)
+- **Confidence:** Probability of predicted class (0.0 to 1.0)
+- **Probabilities:** Dictionary with probabilities for both classes:
+  - `{"entailment": 0.9678, "contradiction": 0.0322}`
+
+## Use Cases
+
+- **Production Fact-Checking:** Fast yes/no contradiction detection for Hebrew summaries
+- **Quality Control:** Automated validation of summary factuality
+- **Batch Processing:** Efficient processing of large document-summary pairs
+- **Real-Time Validation:** Low-latency factuality checking in summary generation pipelines
+
+## Limitations
+
+- Max sequence length: 4,096 tokens (may truncate very long articles)
+- Binary classification: Cannot identify specific error types (use multi-label models for detailed error analysis)
+- Context dependency: Performance may vary with article length and complexity
+- Hebrew-specific: Optimized for Hebrew text; may not generalize to other languages
+
+## Citation
+
+@misc{hebrew_binary_nli_classifier,
+  title={Hebrew Binary NLI Classifier for Factuality Checking},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face}
+}