Sentence Similarity
sentence-transformers
Safetensors
English
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use haf1g/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use haf1g/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("haf1g/result_model") sentences = [ "A man, woman, and child enjoying themselves on a beach.", "A family of three is at the beach.", "There are two woman in this picture.", "There are children present" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from haf1g/result_model: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/haf1g/result_model/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://haf1g/result_model/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/haf1g/result_model/resolve/main/config_sentence_transformers.json
205 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "4.1.0", | |
| "transformers": "4.51.3", | |
| "pytorch": "2.6.0+cu124" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |