Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:898
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Clarkoer/gal-mpnet-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Clarkoer/gal-mpnet-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Clarkoer/gal-mpnet-finetuned") sentences = [ "escape sequences special characters", "**`fertile` (constant) errors:**\n\n**Error: \"Fertile variables must be initialized\"**\n```\n// BAD:\nfertile seed MAX;\n// FIX:\nfertile seed MAX = 100;\n```\n\n**Error: \"Cannot be re-assigned\"**\n```\nfertile s", "loop until condition", "**Escape sequences** in strings:\n\n| Sequence | Result |\n|----------|--------|\n| `\\n` | Newline |\n| `\\t` | Tab |\n| `\\\\` | Backslash `\\` |\n| `\\\"` | Double quote |\n| `\\{` | Literal `{` (in format strings" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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