---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# Resume Matcher Transformer

A fine-tuned sentence transformer model based on `sentence-transformers/all-MiniLM-L6-v2` optimized for comparing resumes with job descriptions.

## Model Overview

This model transforms resumes and job descriptions into 384-dimensional embeddings that can be compared for semantic similarity, helping to identify the best candidates for a position.

### Key Specifications
- **Base Model**: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Output Dimensions**: 384
- **Sequence Length**: 256 tokens maximum
- **Similarity Function**: Cosine Similarity
- **Pooling Strategy**: Mean pooling

## Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
  (2): Normalize()
)
```

## Usage

```bash
# Install the required library
pip install -U sentence-transformers
```

```python
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Load the model
model = SentenceTransformer("path/to/model")

# Example job description
job_description = "Looking for a Python backend developer with Django experience."

# Example resumes
resume1 = "Experienced Python developer with Flask and Django skills."
resume2 = "Teacher with 5 years in classroom management experience."

# Generate embeddings
job_embedding = model.encode(job_description)
resume1_embedding = model.encode(resume1)
resume2_embedding = model.encode(resume2)

# Calculate similarity
similarity1 = cosine_similarity([job_embedding], [resume1_embedding])[0][0]
similarity2 = cosine_similarity([job_embedding], [resume2_embedding])[0][0]

print(f"Similarity with Resume 1: {similarity1:.4f}")
print(f"Similarity with Resume 2: {similarity2:.4f}")
```

## Training Details

### Dataset Information
- **Size**: 4 training samples
- **Format**: Pairs of text samples with similarity labels (0.0 = no match, 1.0 = match)
- **Loss Function**: CosineSimilarityLoss with MSELoss

### Sample Training Data
| Resume/Profile | Job Description | Match Score |
|:--------------|:---------------|:-----------|
| Teacher with classroom management experience | Looking for AI/ML engineer with Python experience | 0.0 |
| DevOps engineer with AWS, Docker, Jenkins | Hiring cloud infrastructure engineer with AWS and CI/CD tools | 1.0 |
| Experienced Python developer with Flask and Django | Looking for backend Python developer with Django experience | 1.0 |

## Training Hyperparameters

- Training epochs: 4
- Batch size: 2
- Learning rate: 5e-05
- Optimizer: AdamW

<details><summary>View all hyperparameters</summary>

- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `num_train_epochs`: 4
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `lr_scheduler_type`: linear
- `warmup_steps`: 0
- `seed`: 42

</details>

## Framework Versions
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Python: 3.11.12

## Citation

```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```