Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2244
loss:MultipleNegativesRankingLoss
Education
Retrieval
Syllabus
text-embeddings-inference
Instructions to use rsajja/Fine-tuned-Educational-Model-MNRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rsajja/Fine-tuned-Educational-Model-MNRL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rsajja/Fine-tuned-Educational-Model-MNRL") sentences = [ "Participation in both lecture and discussion sections is required.", "Service learning: 1.5 to 2 units depending on portfolio evaluation. Must meet 15 service hours for max credit.", "Students are expected to attend both lectures and discussion sessions for full participation credit.", "General info mailbox: replies from Dr. Anil Goyal and Prof. Lucy Salgado." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |