def predict(model, data_loader, ds, label_vocab):
all_preds = []
all_lens = []
for token_ids, lengths, label_ids in data_loader:
preds = model(token_ids, lengths)
all_preds.append(preds.numpy())
all_lens.append(lengths)
sentences = [example[0] for example in ds.data]
results = parse_decodes(sentences, all_preds, all_lens, label_vocab)
return results
def predict(model, data_loader, ds, label_vocab):
all_preds = []
all_lens = []
for input_ids, seg_ids, lens, labels in data_loader:
preds = model(input_ids, seg_ids, lengths=lens)
# Drop CLS prediction
preds = [pred[1:] for pred in preds.numpy()]
all_preds.append(preds)
all_lens.append(lens)
sentences = [example[0] for example in ds.data]
results = parse_decodes(sentences, all_preds, all_lens, label_vocab)
return results