SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full 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_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

• Datasets: dev_evaluation and test_evaluation
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 958 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 958 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 41 tokens mean: 110.12 tokens max: 234 tokens	min: 25 tokens mean: 134.18 tokens max: 256 tokens	min: 0.5 mean: 0.78 max: 0.96

• Samples:

  sentence_0	sentence_1	label
  Required skills include experience with embedding models, fine-tuning techniques, Python programming, and knowledge of NLP concepts. Proficiency in libraries like TensorFlow or PyTorch is essential. Familiarity with resume parsing and matching algorithms is a plus. Must be able to analyze performance metrics and iterate on model improvements.	Skills: Python, TensorFlow, NLP, Embedding Models, Fine-tuning, Resume Matching, Model Evaluation. Experienced in building and deploying machine learning models for text analysis and information retrieval. Proficient in analyzing performance using precision, recall, and F1-score to improve model accuracy.[object Object][object Object]Skills: Python, PyTorch, Natural Language Processing, Text Classification, Machine Learning. Developed several machine learning models using Python and PyTorch for various text related tasks. Good understanding of model evaluation metrics.[object Object][object Object]Technical Skills: Python, Scikit-learn, Data analysis, Data Visualization, Natural Language Processing basics. Projects include text classification and sentiment analysis. Knowledge of model evaluation techniques.[object Object][object Object]Proficient in Python. Familiar with basic machine learning concepts and libraries. Experience with data cleaning and preprocessing. Strong analytical and problem-solving skills.[object Object][object Object]Skills: Python, Pandas, Scikit-learn, Data Preprocessing...	0.8882194757461548
  Experience with embedding models and fine-tuning techniques. Ability to analyze resume data and identify relevant keywords for improved matching. Proficiency in Python and experience with relevant libraries like Transformers, Sentence Transformers, and scikit-learn. Knowledge of A/B testing and evaluation metrics (precision, recall, F1-score). Understanding of product management principles and the product development lifecycle is a plus.	Skills:[object Object]* Python (proficient in Pandas, NumPy)[object Object]* Machine Learning (basic understanding)[object Object]* Data Analysis[object Object]* A/B Testing (conducted tests for website optimization)[object Object]* Excellent communication and presentation skills	0.5
  Senior DevOps Engineer to lead the implementation and optimization of our resume matching system. Responsibilities include: Fine-tuning and evaluating embedding models (e.g., Sentence Transformers, BERT) for improved semantic similarity scoring. Developing and maintaining the infrastructure for model training, evaluation, and deployment. Collaborating with data scientists and software engineers to integrate the matching system into our platform. Monitoring model performance and identifying areas for improvement, including data augmentation strategies. Strong experience with Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines. Must have proficiency in evaluating model performance metrics (precision, recall, F1-score, AUC) and experience with model versioning and A/B testing.	## Experience[object Object][object Object][object Object]	Acme Corp

• Loss: CosineSimilarityLoss with these parameters:
  json

  1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 50
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 4.1.0
• Transformers: 4.51.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.5.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers