Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:16692
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use michaeleliot/claim-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaeleliot/claim-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaeleliot/claim-model") sentences = [ "Al Klug position played tackle", "Alfred Klug position played tackle", "Brad Edwards position played Safety postion", "Michael Jackson's Ghosts position played tackle" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from michaeleliot/claim-model: direct link, hf CLI and curl.
- Browser
- Download file 573 Bytes
-
https://huggingface.co/michaeleliot/claim-model/resolve/main/modules.json
- Command line
-
hf download hf://michaeleliot/claim-model/modules.json
-
curl -L -o modules.json https://huggingface.co/michaeleliot/claim-model/resolve/main/modules.json
573 Bytes
| [ | |
| { | |
| "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_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 4, | |
| "name": "4", | |
| "path": "4_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |