0xnbk/resume-ats-score-v1-en

Resume-ATS Score Dataset v1 (English)

Dataset Description

resume-ats-score-v1-en is a semantic similarity dataset designed for training sentence transformers to predict ATS (Applicant Tracking System) compatibility scores between resumes and job descriptions. This dataset enables fine-tuning models to understand the semantic alignment and matching quality between candidate profiles and job requirements.

Key Features

• 📊 6.4K examples (5.1K train, 1.3K validation)
• 🎯 Continuous ATS scores ranging from 18.3 to 90.7
• 📈 Three-tier classification: No Fit, Potential Fit, Good Fit
• 🔄 Sentence pair format ready for CosineSimilarityLoss training
• ✅ High quality data with 90.5% quality score
• 🌍 Diverse job categories across multiple industries

Dataset Structure

Data Format

Each example contains:

Column	Type	Description
text	string	Combined resume and job description: resume [SEP] job_description
ats_score	float	ATS compatibility score (18.3 - 90.7, normalized to 0-1 for training)
original_label	string	Categorical label: "No Fit", "Potential Fit", or "Good Fit"

Data Splits

Split	Examples	Percentage
Train	5,099	80%
Validation	1,275	20%
Total	6,374	100%

Score Distribution

Metric	Value
Minimum Score	18.3
Maximum Score	90.7
Mean Score	47.2
Median Score	29.9

Label Categories

Label	Score Range	Count	Description
No Fit	< 40	3,457 (54%)	Poor match - significant misalignment
Potential Fit	40-70	1,716 (27%)	Moderate match - some alignment
Good Fit	> 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).

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

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

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:

@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:

@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:

@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
• Trained Model: 0xnbk/nbk-ats-semantic-v1-en
• Domain Classifier Dataset: 0xnbk/resume-domain-classifier-v1-en
• Triplets Dataset: 0xnbk/resume-domain-triplets-train-v1-en
• Application: LOCAL ATS - Privacy-first ATS Resume Analyzer

Contact

For questions, suggestions, or collaboration opportunities:

• GitHub: 0xnbk/localATS
• HuggingFace: @0xnbk
• Website: nbk.dev