Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
new
feature-extraction
Generated from Trainer
dataset_size:32833
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use anudit/finetuned-gte-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anudit/finetuned-gte-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anudit/finetuned-gte-base", trust_remote_code=True) sentences = [ "Anonymity in online interactions can lead to a disinhibition effect, where individuals feel free to express hostile or aggressive opinions they might otherwise suppress.", "What are the implications of anonymity in online interactions?", "How does creativity function as a form of costly signalling in personal expressions such as invitations?", "Why is conflict considered essential in a creative organization?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from anudit/finetuned-gte-base: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/anudit/finetuned-gte-base/resolve/main/modules.json
- Command line
-
hf download hf://anudit/finetuned-gte-base/modules.json
-
curl -L -o modules.json https://huggingface.co/anudit/finetuned-gte-base/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |