yashpwr
/

resume-ner-bert

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 ---
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 ---
+language: en
+license: apache-2.0
 library_name: transformers
+pipeline_tag: token-classification
+tags:
+- resume-parsing
+- named-entity-recognition
+- ner
+- bert
+- information-extraction
+widget:
+- text: "John Doe is a Software Engineer at Google. Email: [email protected], Phone: +1-555-123-4567"
+  example_title: "Resume Information Extraction"
 ---
+# Resume NER Model
+A fine-tuned BERT model for Named Entity Recognition (NER) specifically designed for resume/CV parsing and information extraction.
+## Model Description
+This model is based on `bert-base-cased` and has been fine-tuned to extract key information from resume documents including:
+- label_to_id
+- id_to_label
+## Performance
+| Metric | Score |
+|--------|-------|
+| F1 Score | 0.7128521806252412 |
+| Precision | 0.6843275287143387 |
+| Recall | 0.7438582360048329 |
+| Accuracy | 0.9482567433286769 |
+## Usage
+```python
+from transformers import pipeline
+# Load the model
+ner_pipeline = pipeline(
+    "ner",
+    model="yashpwr/resume-ner-bert",
+    aggregation_strategy="simple"
+)
+# Extract entities from resume text
+text = "John Doe is a Software Engineer at Google. Email: [email protected]"
+results = ner_pipeline(text)
+for entity in results:
+    print(f"{entity['word']}: {entity['entity_group']} ({entity['score']:.3f})")
+```
+## Training Data
+- Training samples: 576
+- Validation samples: 144
+- Epochs: 3
+## Intended Use
+This model is designed for:
+- Resume parsing systems
+- HR automation tools
+- Recruitment platforms
+- Document processing pipelines
+## Limitations
+- Optimized specifically for resume/CV documents
+- Performance may vary on other document types
+- Requires preprocessing for best results
+## Model Details
+- Base model: `bert-base-cased`
+- Model size: ~110M parameters
+- Language: English
+- License: Apache 2.0