LT_training_config.json

{
  "model_save_dir": "models",
  "model_save_name": "linkage_un_data_en_fine_coarse",
  "opt_model_description": "This model was trained on a dataset prepared by linking product classifications from [UN stats](https://unstats.un.org/unsd/classifications/Econ). \n                                 This model is designed to link different products to their coarse product classification - trained on variation brought on by product level correspondance. It was trained for 50 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json \n  ",
  "opt_model_lang": "en",
  "train_batch_size": 64,
  "num_epochs": 50,
  "warm_up_perc": 1,
  "learning_rate": 2e-05,
  "loss_type": "supcon",
  "val_perc": 0.2,
  "wandb_names": {
    "project": "linkage",
    "id": "econabhishek",
    "run": "linkage_un_data_en_fine_coarse",
    "entity": "econabhishek"
  },
  "add_pooling_layer": false,
  "large_val": true,
  "eval_steps_perc": 0.25,
  "test_at_end": true,
  "save_val_test_pickles": true,
  "val_query_prop": 0.5,
  "loss_params": {},
  "warmup_perc": 0.5,
  "eval_type": "retrieval",
  "training_dataset": "dataframe",
  "base_model_path": "multi-qa-mpnet-base-dot-v1",
  "best_model_path": "models/linkage_un_data_en_fine_coarse"
}