contrastive_alignment.py (1232B)

1 from gbure.model.contrastive_alignment import Model 2 from gbure.model.fewshot import Model as EvalModel 3 from torch.optim import Adam as Optimizer 4 from torch.optim.lr_scheduler import LinearLR as Scheduler 5 6 7 dataset_name = "T-REx" 8 graph_name = "T-REx" 9 unsupervised = "triplet" 10 11 eval_dataset_name = "FewRel" 12 valid_name = "7def1330ba9527d6" 13 shot = 1 14 way = 5 15 16 margin = 1 17 neighborhood_size = 3 18 filter_empty_neighborhood = True 19 sinkhorn_blur = 0.05 20 21 # Necessary to make a distance 22 linguistic_similarity = "euclidean" 23 undefined_poison_whole_meta = True 24 25 # From section 4.3 26 blank_probability = 0.7 27 28 # From section 5 29 transformer_model = "bert-base-cased" 30 max_epoch = 10 31 sample_per_epoch = 100000 32 learning_rate = 3e-5 33 accumulated_batch_size = 256 34 clip_gradient = 1 35 36 # Guessed 37 post_transformer_layer = "linear" # Maybe we should change this depending on the subsequent task? 38 max_sentence_length = 100 # Maybe should be 40 (from footnote 2, guessed from ACL slides) 39 language_model_weight = 0 40 edge_sampling = "uniform-inverse degree" 41 42 # From BERT 43 mlm_probability = 0.15 44 mlm_masked_probability = 0.8 45 mlm_random_probability = 0.1 46 47 # Implementation details 48 seed = 0 49 amp = True 50 initial_grad_scale = 1 51 batch_size = 2 52 eval_batch_size = 1 53 workers = 2