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
| { | |
| "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" | |
| } |