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| EXP_NAME: "semsup_descs_100ep_newds_cosine" | |
| EXP_DESC: "SemSup Descriptions ran for 100 epochs" | |
| DATA: | |
| task_name: amazon13k | |
| dataset_name: amazon13k | |
| dataset_config_name: null | |
| max_seq_length: 160 | |
| overwrite_output_dir: false # Set to false, if using one_hour_job | |
| overwrite_cache: false | |
| pad_to_max_length: true | |
| load_from_local: true | |
| max_train_samples: null | |
| max_eval_samples: null | |
| max_predict_samples: null | |
| train_file: datasets/Amzn13K/train_split6500_2.jsonl | |
| validation_file: datasets/Amzn13K/test_unseen_split6500_2.jsonl | |
| test_file: datasets/Amzn13K/test_unseen_split6500_2.jsonl | |
| label_max_seq_length: 160 | |
| # descriptions_file: datasets/Amzn13K/amzn_curie_descsriptions.json | |
| descriptions_file: datasets/Amzn13K/heir_withdescriptions_v3_v3_unseen_final.json | |
| test_descriptions_file: datasets/Amzn13K/heir_withdescriptions_v3_v3.json | |
| all_labels : datasets/Amzn13K/all_labels.txt | |
| test_labels: datasets/Amzn13K/unseen_labels_split6500_2.txt | |
| contrastive_learning_samples: 1000 | |
| cl_min_positive_descs: 1 | |
| # bm_short_file: datasets/eurlex4.3k/train_bmshort.txt | |
| # ignore_pos_labels_file: datasets/Amzn13K/ignore_train_split6500_fs5.txt | |
| coil_cluster_mapping_path: bert_coil_map_dict_lemma255K_isotropic.json | |
| MODEL: | |
| model_name_or_path: bert-base-uncased | |
| # pretrained_model_path: output/semsup_descs_amzn13k_web_6500_small/checkpoint-20000/pytorch_model.bin | |
| config_name: null | |
| tokenizer_name: null | |
| cache_dir: null | |
| use_fast_tokenizer: true | |
| model_revision: main | |
| use_auth_token: false | |
| ignore_mismatched_sizes: false | |
| negative_sampling: "none" | |
| semsup: true | |
| # label_model_name_or_path: bert-base-uncased # prajjwal1/bert-small | |
| label_model_name_or_path: prajjwal1/bert-small | |
| encoder_model_type: bert | |
| use_custom_optimizer: adamw | |
| output_learning_rate: 1.e-4 | |
| arch_type : 2 | |
| add_label_name: true | |
| normalize_embeddings: false | |
| tie_weights: false | |
| coil: false | |
| colbert: false | |
| # use_precomputed_embeddings: datasets/eurlex4.3k/heir_withdescriptions_4.3k_v1_embs_bert_9_96.npy | |
| token_dim: 16 | |
| label_frozen_layers: 2 | |
| TRAINING: | |
| do_train: true | |
| do_eval: true | |
| do_predict: false | |
| per_device_train_batch_size: 1 | |
| gradient_accumulation_steps: 8 | |
| per_device_eval_batch_size: 1 | |
| learning_rate: 5.e-5 # Will point to input encoder lr, if user_custom_optimizer is False | |
| num_train_epochs: 2 | |
| save_steps: 4900 | |
| evaluation_strategy: steps | |
| eval_steps: 3000000 | |
| fp16: true | |
| fp16_opt_level: O1 | |
| lr_scheduler_type: "linear" # defaults to 'linear' | |
| dataloader_num_workers: 16 | |
| label_names: [labels] | |
| scenario: "unseen_labels" | |
| ddp_find_unused_parameters: false | |
| max_eval_samples: 15000 | |
| ignore_data_skip: true | |
| # one_hour_job: true | |
| seed: -1 | |