Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mehdi20002/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mehdi20002/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mehdi20002/result_model") sentences = [ "A couple play in the tide with their young son.", "The family is outside.", "A couple are playing frisbee with a young child at the beach.", "Two adults swimming in water" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "model_specific_special_tokens": {}, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |