Update README.md

README.md

@@ -8,58 +8,156 @@ tags:
 datasets:
 - disham993/ElectricalNER
 metrics:
-- epoch: 1.0
-- eval_precision: 0.8935291782453354
-- eval_recall: 0.9075806451612904
-- eval_f1: 0.9005001000200039
-- eval_accuracy: 0.9586046624222324
-- eval_runtime: 2.509
-- eval_samples_per_second: 601.44
-- eval_steps_per_second: 9.566
+- epoch: 5.0
+- eval_precision: 0.9108
+- eval_recall: 0.9248
+- eval_f1: 0.9177
+- eval_accuracy: 0.9664
+- eval_runtime: 2.121
+- eval_samples_per_second: 711.447
+- eval_steps_per_second: 11.315
 ---
+# electrical-ner-ModernBERT-base
 
-# disham993/electrical-ner-modernbert-base
+## Model Description
 
-## Model description
-
-This model is fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) for token-classification tasks.
+This model is fine-tuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-base) for token-classification tasks, specifically Named Entity Recognition (NER) in the electrical engineering domain. The model has been optimized to extract entities such as components, materials, standards, and design parameters from technical texts with high precision and recall.
 
 ## Training Data
 
-The model was trained on the disham993/ElectricalNER dataset. 
+The model was trained on the [disham993/ElectricalNER](https://huggingface.co/datasets/disham993/ElectricalNER) dataset, a GPT-4o-mini-generated dataset curated for the electrical engineering domain. This dataset includes diverse technical contexts, such as circuit design, testing, maintenance, installation, troubleshooting, or research.
 
 ## Model Details
-- **Base Model:** answerdotai/ModernBERT-base
-- **Task:** token-classification
-- **Language:** en
-- **Dataset:** disham993/ElectricalNER
 
-## Training procedure
+- **Base Model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+- **Task:** Token Classification (NER)
+- **Language:** English (en)
+- **Dataset:** [disham993/ElectricalNER](https://huggingface.co/datasets/disham993/ElectricalNER)
+
+## Training Procedure
+
+### Training Hyperparameters
+
+The model was fine-tuned using the following hyperparameters:
+
+- **Evaluation Strategy:** epoch
+- **Learning Rate:** 1e-5
+- **Batch Size:** 64 (for both training and evaluation)
+- **Number of Epochs:** 5
+- **Weight Decay:** 0.01
 
-### Training hyperparameters
-[Please add your training hyperparameters here]
+## Evaluation Results
 
-## Evaluation results
+The following metrics were achieved during evaluation:
 
-### Metrics\n- epoch: 1.0\n- eval_precision: 0.8935291782453354\n- eval_recall: 0.9075806451612904\n- eval_f1: 0.9005001000200039\n- eval_accuracy: 0.9586046624222324\n- eval_runtime: 2.509\n- eval_samples_per_second: 601.44\n- eval_steps_per_second: 9.566
+- **Precision:** 0.9108
+- **Recall:** 0.9248
+- **F1 Score:** 0.9177
+- **Accuracy:** 0.9664
+- **Evaluation Runtime:** 2.121 seconds
+- **Samples Per Second:** 711.447
+- **Steps Per Second:** 11.315
 
 ## Usage
 
-```python
-from transformers import AutoTokenizer, AutoModel
+You can use this model for Named Entity Recognition tasks as follows:
 
-tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-ner-modernbert-base")
-model = AutoModel.from_pretrained("disham993/electrical-ner-modernbert-base")
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+model_name = "disham993/electrical-ner-modernbert-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+text = "The Xilinx Vivado development suite was used to program the Artix-7 FPGA."
+
+ner_results = nlp(text)
+
+def clean_and_group_entities(ner_results, min_score=0.40):
+    """
+    Cleans and groups named entity recognition (NER) results based on a minimum score threshold.
+    
+    Args:
+        ner_results (list of dict): A list of dictionaries containing NER results. Each dictionary should have the keys:
+            - "word" (str): The recognized word or token.
+            - "entity_group" (str): The entity group or label.
+            - "start" (int): The start position of the entity in the text.
+            - "end" (int): The end position of the entity in the text.
+            - "score" (float): The confidence score of the entity recognition.
+        min_score (float, optional): The minimum score threshold for considering an entity. Defaults to 0.40.
+    
+    Returns:
+        list of dict: A list of grouped entities that meet the minimum score threshold. Each dictionary contains:
+            - "entity_group" (str): The entity group or label.
+            - "word" (str): The concatenated word or token.
+            - "start" (int): The start position of the entity in the text.
+            - "end" (int): The end position of the entity in the text.
+            - "score" (float): The minimum confidence score of the grouped entity.
+    """
+    grouped_entities = []
+    current_entity = None
+
+    for result in ner_results:
+        # Skip entities with score below threshold
+        if result["score"] < min_score:
+            if current_entity:
+                # Add current entity if it meets threshold
+                if current_entity["score"] >= min_score:
+                    grouped_entities.append(current_entity)
+                current_entity = None
+            continue
+
+        word = result["word"].replace("##", "")  # Remove subword token markers
+        
+        if current_entity and result["entity_group"] == current_entity["entity_group"] and result["start"] == current_entity["end"]:
+            # Continue the current entity
+            current_entity["word"] += word
+            current_entity["end"] = result["end"]
+            current_entity["score"] = min(current_entity["score"], result["score"])
+            
+            # If combined score drops below threshold, discard the entity
+            if current_entity["score"] < min_score:
+                current_entity = None
+        else:
+            # Finalize the current entity if it meets threshold
+            if current_entity and current_entity["score"] >= min_score:
+                grouped_entities.append(current_entity)
+            
+            # Start a new entity
+            current_entity = {
+                "entity_group": result["entity_group"],
+                "word": word,
+                "start": result["start"],
+                "end": result["end"],
+                "score": result["score"]
+            }
+
+    # Add the last entity if it meets threshold
+    if current_entity and current_entity["score"] >= min_score:
+        grouped_entities.append(current_entity)
+
+    return grouped_entities
+
+cleaned_results = clean_and_group_entities(ner_results)
 ```
 
-## Limitations and bias
+## Limitations and Bias
+
+While this model performs well in the electrical engineering domain, it is not designed for use in other domains. Additionally, it may:
+
+- Misclassify entities due to potential inaccuracies in the GPT-4o-mini-generated dataset.
+- Struggle with ambiguous contexts or low-confidence predictions - this is minimized with help of `clean_and_group_entities` function.
+
+This model is intended for research and educational purposes only, and users are encouraged to validate results before applying them to critical applications.
 
-[Add any known limitations or biases of the model]
+Users are encouraged to validate results for critical applications.
 
 ## Training Infrastructure
 
-[Add details about training infrastructure used]
+For a complete guide covering the entire process - from data tokenization to pushing the model to the Hugging Face Hub - please refer to the [GitHub repository](https://github.com/di37/ner-electrical-finetuning).
 
-## Last update
+## Last Update
 
-2024-12-30
+2024-12-30