Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:105
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Aju360/ats-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Aju360/ats-mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Aju360/ats-mpnet") sentences = [ "Healthcare Analyst with 8 years of experience. Skilled in Healthcare Data, SQL, Reporting, Analytics. Delivered projects and collaborated across teams.", "Data Scientist position. Required skills include Python, Machine Learning, B2B Sales, CRM. Looking for a candidate with strong communication and execution skills.", "Sales Manager position. Required skills include B2B Sales, CRM, Negotiation, Leadership. Looking for a candidate with strong communication and execution skills.", "DevOps Engineer position. Required skills include AWS, Kubernetes, Docker, CI/CD. Looking for a candidate with strong communication and execution skills." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "do_lower_case": true, | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "max_length": 128, | |
| "model_max_length": 384, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "<pad>", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "</s>", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "MPNetTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |