Update README.md

README.md

@@ -120,133 +120,6 @@ print(f"Probabilities: {probabilities}")For detailed inference examples, see the
 
 ## 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},