Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Trained with AutoTrain
text-embeddings-inference
Instructions to use PyxiLab/Pyx-embeds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PyxiLab/Pyx-embeds with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PyxiLab/Pyx-embeds") sentences = [ "search_query: i love autotrain", "search_query: huggingface auto train", "search_query: hugging face auto train", "search_query: i love autotrain" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 949 Bytes
d0d74c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"data_path": "sentence-transformers/all-nli",
"model": "sentence-transformers/all-MiniLM-L6-v2",
"lr": 3e-05,
"epochs": 3,
"max_seq_length": 128,
"batch_size": 8,
"warmup_ratio": 0.1,
"gradient_accumulation": 1,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.0,
"max_grad_norm": 1.0,
"seed": 42,
"train_split": "triplet:train",
"valid_split": "triplet:dev",
"logging_steps": -1,
"project_name": "autotrain-q1ygq-0lob5",
"auto_find_batch_size": false,
"mixed_precision": "fp16",
"save_total_limit": 1,
"push_to_hub": true,
"eval_strategy": "epoch",
"username": "PyxiLabs",
"log": "tensorboard",
"early_stopping_patience": 5,
"early_stopping_threshold": 0.01,
"trainer": "triplet",
"sentence1_column": "anchor",
"sentence2_column": "positive",
"sentence3_column": "negative",
"target_column": "target"
} |