Instructions to use aidanlli/posmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aidanlli/posmodel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aidanlli/posmodel") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_save_dir": "models", | |
| "model_save_name": "linkage", | |
| "opt_model_description": null, | |
| "opt_model_lang": null, | |
| "train_batch_size": 64, | |
| "num_epochs": 1, | |
| "warm_up_perc": 1, | |
| "learning_rate": 2e-05, | |
| "loss_type": "supcon", | |
| "val_perc": 0.2, | |
| "wandb_names": { | |
| "project": "linktransformer", | |
| "id": "your-id", | |
| "run": "run-name", | |
| "entity": "your-id" | |
| }, | |
| "add_pooling_layer": false, | |
| "large_val": true, | |
| "eval_steps_perc": 0.5, | |
| "test_at_end": true, | |
| "save_val_test_pickles": true, | |
| "val_query_prop": 0.5, | |
| "loss_params": {}, | |
| "eval_type": "retrieval", | |
| "training_dataset": "/content/drive/My Drive/korea/panel_task/linktransformer_postest.csv", | |
| "base_model_path": "oshizo/sbert-jsnli-luke-japanese-base-lite", | |
| "best_model_path": "models/linkage" | |
| } |