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

Co-authored-by: nbk <0xnbk@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,62 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+ats_data_fixed.csv filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text
+validation.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,351 @@
+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- sentence-similarity
+tags:
+- resume
+- job-matching
+- ats
+- semantic-similarity
+- sentence-transformers
+- cosine-similarity
+size_categories:
+- 1K<n<10K
+---
+
+# 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](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)

ats_data_fixed_quality_report.json

@@ -0,0 +1,39 @@
+{
+  "total_samples": 6865,
+  "validation_results": {
+    "empty_texts": "0",
+    "duplicates": "2"
+  },
+  "statistics": {
+    "score_distribution": {
+      "count": 6865.0,
+      "mean": 47.18647050254916,
+      "std": 24.941265006867923,
+      "min": 18.28,
+      "25%": 24.8,
+      "50%": 29.9,
+      "75%": 59.8,
+      "max": 90.68
+    },
+    "text_length_distribution": {
+      "count": 6865.0,
+      "mean": 8480.135906773488,
+      "std": 3300.6841604711535,
+      "min": 1427.0,
+      "25%": 6227.0,
+      "50%": 7869.0,
+      "75%": 10067.0,
+      "max": 22567.0
+    },
+    "label_distribution": {
+      "No Fit": 3457,
+      "Potential Fit": 1716,
+      "Good Fit": 1692
+    }
+  },
+  "quality_issues": [
+    "Found 2 duplicate entries",
+    "Found 6553 very long texts (>4000 chars)"
+  ],
+  "overall_quality_score": 90.45
+}

dataset_metadata.json

@@ -0,0 +1,47 @@
+{
+  "dataset_name": "resume-ats-score-v1-en",
+  "version": "1.0.0",
+  "description": "Semantic similarity training dataset for fine-tuning sentence transformers to predict ATS (Applicant Tracking System) compatibility scores between resumes and job descriptions",
+  "total_examples": 6374,
+  "train_examples": 5099,
+  "validation_examples": 1275,
+  "task": "sentence_similarity",
+  "format": "text_pairs_with_scores",
+  "input_format": "resume [SEP] job_description",
+  "score_range": {
+    "min": 18.28,
+    "max": 90.68,
+    "mean": 47.19,
+    "median": 29.9
+  },
+  "label_categories": {
+    "No Fit": "ATS score < 40 (poor match)",
+    "Potential Fit": "ATS score 40-70 (moderate match)",
+    "Good Fit": "ATS score > 70 (strong match)"
+  },
+  "label_distribution": {
+    "No Fit": 3457,
+    "Potential Fit": 1716,
+    "Good Fit": 1692
+  },
+  "training_objective": "CosineSimilarityLoss for learning semantic alignment between resumes and job requirements",
+  "use_case": "Fine-tuning sentence transformers for ATS score prediction and resume-job matching",
+  "source": "Derived from Resume-Job Description Fit dataset (cnamuangtoun/resume-job-description-fit)",
+  "source_url": "https://huggingface.co/datasets/cnamuangtoun/resume-job-description-fit",
+  "generation_method": "Paired resume-job descriptions with calculated ATS compatibility scores based on semantic similarity",
+  "generation_date": "2025-10-13",
+  "language": "en",
+  "license": "Apache-2.0",
+  "intended_use": "Training sentence transformers for semantic similarity and ATS score prediction",
+  "model_trained": "nbk-ats-semantic-v1-en",
+  "preprocessing": "Text cleaned and normalized, duplicates removed, combined in sentence pair format with similarity scores",
+  "quality_metrics": {
+    "empty_texts": 0,
+    "duplicates_removed": 2,
+    "overall_quality_score": 90.45
+  },
+  "split_ratio": {
+    "train": 0.8,
+    "validation": 0.2
+  }
+}

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a96c1c53e5f6733428bb6b632d776d15c4dfba31b6367f321b84c043937c1e12
+size 43316484

validation.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b519a2bec8bc2581b2b399310737cd1cdbb24d1fcc1284974e0ded1e51bd3d
+size 10681275