Token Classification
Transformers
ONNX
Safetensors
English
distilbert
resume-parsing
ner
resume
cv
information-extraction
Instructions to use oksomu/resume-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oksomu/resume-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="oksomu/resume-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("oksomu/resume-ner") model = AutoModelForTokenClassification.from_pretrained("oksomu/resume-ner") - Notebooks
- Google Colab
- Kaggle
| import unittest | |
| from training.dataset_utils import dedupe_examples, stable_split_examples | |
| class DatasetUtilsTest(unittest.TestCase): | |
| def test_dedupe_examples_removes_exact_duplicates(self): | |
| example = {"tokens": ["A"], "ner_tags": [0], "metadata": {"group_id": "x"}} | |
| unique, removed = dedupe_examples([example, dict(example)]) | |
| self.assertEqual(len(unique), 1) | |
| self.assertEqual(removed, 1) | |
| def test_stable_split_keeps_group_together(self): | |
| examples = [ | |
| {"tokens": ["A"], "ner_tags": [0], "metadata": {"group_id": "same"}}, | |
| {"tokens": ["B"], "ner_tags": [0], "metadata": {"group_id": "same"}}, | |
| {"tokens": ["C"], "ner_tags": [0], "metadata": {"group_id": "other"}}, | |
| ] | |
| train, val = stable_split_examples(examples, train_ratio=0.5) | |
| sides = [] | |
| if any(example["metadata"]["group_id"] == "same" for example in train): | |
| sides.append("train") | |
| if any(example["metadata"]["group_id"] == "same" for example in val): | |
| sides.append("val") | |
| self.assertEqual(len(sides), 1) | |
| if __name__ == "__main__": | |
| unittest.main() | |