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 MahfoudAi/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MahfoudAi/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MahfoudAi/result_model") sentences = [ "Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.", "A man and a soman are eating together at John's Pizza and Gyro.", "A high school is hosting an event.", "A family of three is at the beach." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 394 Bytes
e21dc02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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>"
}
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