Update weights and training params

Update the weights and training args after retraining with more negative samples

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:86765e84432693b35e89473e7909de8421ba5a33562964d0d974a4bbfb302bcd
+oid sha256:d14a1076b356024cbd18766d19bd907d6df0d48a6f219687e092058951a39b8e
 size 498604904

training_args.txt

@@ -1,5 +1,5 @@
-step = 16500
+step = 20000
 _metadata = ContainerMetadata(ref_type=typing.Any, object_type=<class 'dict'>, optional=True, key=None, flags={}, flags_root=False, resolver_cache=defaultdict(<class 'dict'>, {}), key_type=typing.Any, element_type=typing.Any)
 _parent = None
 _flags_cache = {'struct': None}
-_content = {'project_name': 'coq-theorem-embedding', 'experiment_name': 'infonce-file-final-1', 'log_level': 'INFO', 'dataset': {'dataset_path': './data/', 'rankin_ds_path_statements': './data/imm/basic/Events_statements.json', 'rankin_ds_path_references': './validationSet/reference_premises.json', 'samples_from_single_anchor': 150, 'train_split': 0.8, 'val_split': 0.2, 'test_split': 0.0}, 'base_model_name': 'microsoft/codebert-base', 'max_seq_length': 128, 'embedding_dim': 768, 'threshold_pos': 0.3, 'threshold_neg': 0.65, 'threshold_hard_neg': 0.45, 'k_negatives': 4, 'steps': 30000, 'batch_size': 32, 'warmup_ratio': 0.1, 'learning_rate': 4e-06, 'random_seed': 52, 'wandb': {'enabled': True, 'project': 'coq-embeddings', 'tags': ['dyn-lr', 'InfoNCE', 'hard-negatives', 'codebert']}, 'evaluation': {'output_dir': './checkpoints/', 'save_freq': 300, 'eval_steps': 200, 'evaluate_freq': 100, 'k_values': [5, 10], 'f_score_beta': 1, 'query_size_in_eval': 20}}
+_content = {'project_name': 'coq-theorem-embedding', 'experiment_name': 'test-run-many-negatives', 'log_level': 'INFO', 'dataset': {'dataset_path': './data/', 'rankin_ds_path_statements': './data/imm/basic/Events_statements.json', 'rankin_ds_path_references': './sanityCheckSet/reference_premises.json', 'samples_from_single_anchor': 300, 'train_split': 0.7, 'val_split': 0.2, 'test_split': 0.1}, 'base_model_name': 'microsoft/codebert-base', 'max_seq_length': 128, 'embedding_dim': 768, 'threshold_pos': 0.3, 'threshold_neg': 0.65, 'threshold_hard_neg': 0.45, 'k_negatives': 100, 'steps': 22000, 'batch_size': 16, 'warmup_ratio': 0.1, 'learning_rate': 4e-06, 'random_seed': 52, 'wandb': {'enabled': True, 'project': 'coq-embeddings', 'entity': 'kozyrev-andreiii2016', 'tags': ['dyn-lr', 'InfoNCE', 'hard-negatives', 'codebert']}, 'evaluation': {'output_dir': './checkpoints/', 'save_freq': 1000, 'eval_steps': 200, 'evaluate_freq': 200, 'k_values': [5, 10], 'f_score_beta': 1, 'query_size_in_eval': 20}}