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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: 128
overwrite_output_dir: true # 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_split1668_highfreq_fs50.jsonl
# train_file: datasets/Amzn13K/train_split1668_highfreq.jsonl
train_file: datasets/Amzn13K/train_split1106_highfreq_bot.jsonl
validation_file: datasets/Amzn13K/test_unseen_split6500_2.jsonl
test_file: datasets/Amzn13K/test_unseen_split6500_2.jsonl
label_max_seq_length: 96
# descriptions_file: datasets/Amzn13K/amzn_curie_descsriptions.json
# descriptions_file: datasets/Amzn13K/amzn_descs_refined_v3_v3.json
descriptions_file: datasets/Amzn13K/heir_withdescriptions_v3_v3_unseen.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
# max_descs_per_label: 10
# contrastive_learning_samples: 5000
# cl_min_positive_descs: 1
# bm_short_file: datasets/eurlex4.3k/train_bmshort.txt
# ignore_pos_labels_file: datasets/Amzn13K/ignore_train_split1668_highfreq_fs50.txt
MODEL:
model_name_or_path: bert-base-uncased
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: true
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: 4
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: 20
save_steps: 5000
evaluation_strategy: steps
eval_steps: 1000
fp16: true
fp16_opt_level: O1
lr_scheduler_type: "linear" # defaults to 'linear'
dataloader_num_workers: 8
label_names: [labels]
scenario: "unseen_labels"
ddp_find_unused_parameters: false
max_eval_samples: 15000
max_train_samples: 30000
# ignore_data_skip: true
# one_hour_job: true
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