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#loading WSD (semcor) data + convert to supersenses
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train_path = os.path.join(args.data_path, 'Training_Corpora/SemCor/')
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train_data = load_data(train_path, 'semcor')
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#filter train data for k-shot learning
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if args.kshot > 0:
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train_data = filter_k_examples(train_data, args.kshot)
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task_labels, label_map = get_label_space(train_data)
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print('num labels = {} + 1 unknown label'.format(len(task_labels)-1))
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train_data = preprocess(tokenizer, pretrained_model, train_data, task_labels, label_map)
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train_data = batchify(train_data, bsz=args.bsz)
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num_epochs = args.epochs
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if args.kshot > 0:
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NUM_STEPS = 176600 #hard coded for fair comparision with full model on default num. of epochs
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num_batches = len(train_data)
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num_epochs = NUM_STEPS//num_batches #recalculate number of epochs
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overflow_steps = NUM_STEPS%num_batches #num steps in last overflow epoch (if there is one, otherwise 0)
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t_total = NUM_STEPS #manually set number of steps for lr schedule
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if overflow_steps > 0: num_epochs+=1 #add extra epoch for overflow steps
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print('Overriding args.epochs and training for {} epochs...'.format(epochs))
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#loading eval data & convert to supersense tags
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#dev set = semeval2007
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semeval2007_path = os.path.join(args.data_path, 'Evaluation_Datasets/semeval2007/')
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semeval2007_data = load_data(semeval2007_path, 'semeval2007')
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semeval2007_data = preprocess(tokenizer, pretrained_model, semeval2007_data, task_labels, label_map)
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semeval2007_data = batchify(semeval2007_data, bsz=1)
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'''
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SET UP PROBING MODEL FOR TASK
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'''
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#probing model = projection layer to label space, loss function, and optimizer
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probe = torch.nn.Linear(output_dim, len(task_labels))
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probe = probe.cuda()
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criterion = torch.nn.CrossEntropyLoss(reduction='none')
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optim = torch.optim.Adam(probe.parameters(), lr=lr)
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'''
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TRAIN PROBING MODEL ON SEMCOR DATA
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'''
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best_dev_f1 = 0.
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print('Training probe...')
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sys.stdout.flush()
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for epoch in range(1, num_epochs+1):
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#train on full dataset
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probe_optim = _train(train_data, probe, optim, criterion, bsz=bsz, silent=args.silent)
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#eval probe on dev set (semeval2007)
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eval_preds = _eval(semeval2007_data, probe, task_labels)
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#generate predictions file
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pred_filepath = os.path.join(args.ckpt, 'tmp_predictions.txt')
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with open(pred_filepath, 'w') as f:
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for inst, prediction in eval_preds:
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f.write('{} {}\n'.format(inst, prediction))
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#run predictions through scorer
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gold_filepath = os.path.join(args.data_path, 'Evaluation_Datasets/semeval2007/semeval2007.gold.key.txt')
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scorer_path = os.path.join(args.data_path, 'Evaluation_Datasets')
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_, _, dev_f1 = evaluate_output(scorer_path, gold_filepath, pred_filepath)
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print('Dev f1 after {} epochs = {}'.format(epoch, dev_f1))
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sys.stdout.flush()
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if dev_f1 >= best_dev_f1:
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print('updating best model at epoch {}...'.format(epoch))
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sys.stdout.flush()
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best_dev_f1 = dev_f1
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#save to file if best probe so far on dev set
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probe_fname = os.path.join(args.ckpt, 'best_model.ckpt')
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with open(probe_fname, 'wb') as f:
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torch.save(probe.state_dict(), f)
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sys.stdout.flush()
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#shuffle train data after every epoch
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random.shuffle(train_data)
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return
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def evaluate_probe(args):
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print('Evaluating WSD probe on {}...'.format(args.split))
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'''
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LOAD TOKENIZER + BERT MODEL
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'''
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tokenizer = load_tokenizer(args.encoder_name)
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pretrained_model, output_dim = load_pretrained_model(args.encoder_name)
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pretrained_model = pretrained_model.cuda()
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'''
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GET LABEL SPACE
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'''
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train_path = os.path.join(args.data_path, 'Training_Corpora/SemCor/')
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train_data = load_data(train_path, 'semcor')
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task_labels, label_map = get_label_space(train_data)
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#for backoff eval
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