--- language: - en license: apache-2.0 task_categories: - sentence-similarity tags: - resume - job-matching - ats - semantic-similarity - sentence-transformers - cosine-similarity size_categories: - 1K 70 | 1,692 (27%) | Strong match - high compatibility | ### Example Data Points **Good Fit (Score: 80.6):** ``` Resume: "Software Engineer with 17 years IT experience, expert in .NET, C#, ASP.NET MVC..." Job: "Software Engineering Manager requiring technical leadership, .NET, C#, web development..." ATS Score: 80.6 Label: Good Fit ``` **Potential Fit (Score: 53.9):** ``` Resume: "Sales Associate with customer service experience, Windows/Linux knowledge..." Job: "Software Developer position requiring C++, Qt, web development..." ATS Score: 53.9 Label: Potential Fit ``` **No Fit (Score: 24.3):** ``` Resume: "Web Developer with PHP, JavaScript, CSS experience..." Job: "Software Engineering Manager requiring 5+ years management, team leadership..." ATS Score: 24.3 Label: No Fit ``` ## Source Data This dataset is derived from the **Resume-Job Description Fit** dataset ([cnamuangtoun/resume-job-description-fit](https://huggingface.co/datasets/cnamuangtoun/resume-job-description-fit)). ### Data Generation Process 1. **Source Extraction**: Resume-job pairs extracted from base dataset 2. **Quality Filtering**: - Removed empty texts (0 found) - Removed duplicates (2 removed) - Filtered very short or very long texts 3. **Score Calculation**: ATS compatibility scores computed based on semantic similarity 4. **Normalization**: Text cleaned and normalized 5. **Categorization**: Scores categorized into No Fit, Potential Fit, Good Fit 6. **Train/Val Split**: 80/20 split for model training and evaluation ### Quality Metrics - **Empty Texts**: 0 (100% complete) - **Duplicates Removed**: 2 - **Overall Quality Score**: 90.45% - **Average Text Length**: ~8,480 characters per example ## Intended Use ### Primary Use Cases 1. **ATS Score Prediction**: Train models to predict compatibility between resumes and jobs 2. **Semantic Similarity Learning**: Fine-tune sentence transformers for resume-job matching 3. **Resume Ranking**: Rank candidates based on job description fit 4. **Job Recommendation**: Recommend suitable jobs for candidate profiles ### Model Training This dataset is designed for training with **CosineSimilarityLoss** using sentence transformers: **Recommended Base Models:** - `jinaai/jina-embeddings-v2-base-en` (used for nbk-ats-semantic-v1-en) - `sentence-transformers/all-MiniLM-L6-v2` - `sentence-transformers/all-mpnet-base-v2` - Any sentence transformer model **Expected Performance**: Models trained on this dataset typically achieve **RMSE < 8.0** for ATS score prediction. ### Example Training Code ```python from sentence_transformers import SentenceTransformer, losses, InputExample from torch.utils.data import DataLoader from datasets import load_dataset import pandas as pd # Load dataset dataset = load_dataset("0xnbk/resume-ats-score-v1-en") train_df = pd.DataFrame(dataset['train']) # Prepare training examples with normalized scores (0-1 range) train_examples = [] for _, row in train_df.iterrows(): # Split resume and job description resume, job = row['text'].split(' SEP ') # Normalize score to 0-1 range for CosineSimilarityLoss normalized_score = row['ats_score'] / 100.0 train_examples.append( InputExample(texts=[resume, job], label=normalized_score) ) # Load base model model = SentenceTransformer('jinaai/jina-embeddings-v2-base-en') # Define loss and dataloader train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16) train_loss = losses.CosineSimilarityLoss(model=model) # Train model.fit( train_objectives=[(train_dataloader, train_loss)], epochs=4, warmup_steps=100, optimizer_params={'lr': 2e-5}, output_path='./ats-semantic-model' ) # Save model.save('./ats-semantic-model') ``` ### Inference Example ```python from sentence_transformers import SentenceTransformer from scipy.spatial.distance import cosine # Load trained model model = SentenceTransformer('./ats-semantic-model') # Test resume-job matching resume = "Software engineer with 5 years Python, Django, React experience" job_good_fit = "Senior Python Developer requiring Django framework expertise" job_poor_fit = "Registered nurse position requiring ICU patient care" # Encode resume_emb = model.encode(resume) good_fit_emb = model.encode(job_good_fit) poor_fit_emb = model.encode(job_poor_fit) # Calculate ATS scores (cosine similarity * 100) good_fit_score = (1 - cosine(resume_emb, good_fit_emb)) * 100 poor_fit_score = (1 - cosine(resume_emb, poor_fit_emb)) * 100 print(f"Good fit ATS score: {good_fit_score:.1f}") # Expected: 70-90 print(f"Poor fit ATS score: {poor_fit_score:.1f}") # Expected: 20-40 ``` ## Dataset Statistics ### Size Metrics - **Total size**: ~51MB (CSV format with text pairs) - **Average text length**: ~8,480 characters per example - **Average resume length**: ~4,500 characters - **Average job description length**: ~3,980 characters - **Token count**: ~7M tokens (estimated with BERT tokenizer) ### Score Distribution Analysis The dataset shows a realistic distribution of resume-job matching: - **Peak at low scores** (20-30 range): Many resumes don't closely match specific jobs - **Second peak at high scores** (70-90 range): Well-matched professional pairs - **Moderate scores** (40-70 range): Partial skill overlap or transferable experience This distribution reflects real-world ATS screening where most candidates are filtered out, some show potential, and a smaller portion are strong matches. ## Training Details ### Model: nbk-ats-semantic-v1-en This dataset was used to train the **nbk-ats-semantic-v1-en** model with the following configuration: - **Base Model**: jinaai/jina-embeddings-v2-base-en (fine-tuned for semantic similarity) - **Loss Function**: CosineSimilarityLoss with normalized scores (0-1 range) - **Epochs**: 4 - **Batch Size**: 16 - **Learning Rate**: 2e-5 - **Warmup Steps**: 100 - **Hardware**: NVIDIA A6000 48GB GPU - **Training Time**: ~30 minutes ### Performance Metrics - **RMSE**: < 8.0 (excellent prediction accuracy) - **R² Score**: > 0.85 (strong predictive power) - **MAE**: < 6.0 (low average error) - **Pearson Correlation**: > 0.9 (excellent linear relationship) ## Limitations and Considerations ### Known Limitations 1. **Score Subjectivity**: ATS scores are calculated algorithmically and may not reflect human judgment 2. **Domain Coverage**: Dataset may not cover all niche industries or specialized roles 3. **Language**: Currently only English language support 4. **Text Length**: Long resumes and job descriptions (average ~8,500 chars) may challenge some models 5. **Temporal Bias**: Reflects job market terminology as of 2024-2025 ### Ethical Considerations - **Bias**: May reflect biases present in resume screening and job posting practices - **Privacy**: No personally identifiable information (PII) included - **Fairness**: Users should validate model fairness across protected characteristics - **Transparency**: Scores are algorithmically derived, not human-annotated - **Responsible Use**: Should supplement, not replace, human judgment in hiring decisions ## Citation If you use this dataset in your research or applications, please cite: ```bibtex @dataset{resume_ats_score_v1, author = {NBK}, title = {Resume-ATS Score Dataset v1 (English)}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/0xnbk/resume-ats-score-v1-en} } ``` ### Source Dataset Citation This dataset is derived from the Resume-Job Description Fit dataset: ```bibtex @dataset{resume_job_description_fit, author = {cnamuangtoun}, title = {Resume-Job Description Fit}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/cnamuangtoun/resume-job-description-fit} } ``` ### Model Citation If you use the model trained on this dataset: ```bibtex @model{nbk_ats_semantic_v1, author = {NBK}, title = {NBK ATS Semantic Model v1 (English)}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/0xnbk/nbk-ats-semantic-v1-en} } ``` ## License This dataset is released under the **Apache 2.0 License**. ``` Copyright 2025 NBK (nbk.dev) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ``` ## Updates and Maintenance - **Version**: 1.0.0 - **Last Updated**: October 2025 - **Maintained by**: NBK (nbk.dev) - **Issues**: Report issues on the dataset discussion page ## Related Resources - **Source Dataset**: [cnamuangtoun/resume-job-description-fit](https://huggingface.co/datasets/cnamuangtoun/resume-job-description-fit) - **Trained Model**: [0xnbk/nbk-ats-semantic-v1-en](https://huggingface.co/0xnbk/nbk-ats-semantic-v1-en) - **Domain Classifier Dataset**: [0xnbk/resume-domain-classifier-v1-en](https://huggingface.co/datasets/0xnbk/resume-domain-classifier-v1-en) - **Triplets Dataset**: [0xnbk/resume-domain-triplets-train-v1-en](https://huggingface.co/datasets/0xnbk/resume-domain-triplets-train-v1-en) - **Application**: [LOCAL ATS](https://github.com/0xnbk/localATS) - Privacy-first ATS Resume Analyzer ## Contact For questions, suggestions, or collaboration opportunities: - **GitHub**: [0xnbk/localATS](https://github.com/0xnbk/localATS) - **HuggingFace**: [@0xnbk](https://huggingface.co/0xnbk) - **Website**: [nbk.dev](https://nbk.dev)