Bert

Pipfile

@@ -18,6 +18,8 @@ scikit-learn = "*"
 torch = "*"
 pytorch-ignite = "*"
 torchtext = "*"
+pytorch-pretrained-bert = "*"
+pandas = "*"
 
 [requires]
 python_version = "3.6"

bin/test.py

@@ -10,9 +10,9 @@
 
 from torch.utils.data import DataLoader
 
+from src.vocab import get_vocab
 from src.matching_network import MatchingNetwork
 from src.evaluation import (predict, save_predictions, generate_episode_data)
-from src.data import read_vocab, read_data_set
 from src.datasets import EpisodesSampler, EpisodesDataset
 from src.utils import extract_model_parameters, get_model_name
 
@@ -50,11 +50,11 @@
 parser.add_argument("test_set", help="Path to the test CSV file")
 
 
-def _load_model(model_path):
+def _load_model(model_path, vocab):
     model_file_name = os.path.basename(args.model)
     distance, embeddings, N, k = extract_model_parameters(model_file_name)
     model_name = get_model_name(distance, embeddings, N, k)
-    model = MatchingNetwork(model_name, distance_metric=distance)
+    model = MatchingNetwork(model_name, vocab, distance_metric=distance)
     model_state_dict = torch.load(model_path)
     model.load_state_dict(model_state_dict)
 
@@ -63,11 +63,11 @@ def _load_model(model_path):
 
 def main(args):
     print("Loading model...")
-    model, _, N, k = _load_model(args.model)
+    vocab = get_vocab(args.embeddings, args.vocab)
+    model, embeddings, N, k = _load_model(args.model, vocab)
 
     print("Loading dataset...")
-    vocab = read_vocab(args.vocab)
-    X_test, y_test = read_data_set(args.test_set, vocab)
+    X_test, y_test = vocab.to_tensors(args.test_set)
     test_set = EpisodesDataset(X_test, y_test, k=k)
     sampler = EpisodesSampler(test_set, N=N, episodes_multiplier=30)
     test_loader = DataLoader(test_set, sampler=sampler, batch_size=BATCH_SIZE)

bin/train.py

@@ -5,7 +5,7 @@
 
 from torch.utils.data import DataLoader
 
-from src.data import read_vocab, read_data_set
+from src.vocab import get_vocab
 from src.datasets import EpisodesSampler, EpisodesDataset
 from src.matching_network import MatchingNetwork
 from src.training import train
@@ -44,6 +44,14 @@
     type=str,
     default='cosine',
     help="Distance metric to be used")
+parser.add_argument(
+    "-e",
+    "--embeddings",
+    action="store",
+    dest="embeddings",
+    type=str,
+    default='vanilla',
+    help="Type of embedding")
 parser.add_argument(
     "-p",
     "--processing-steps",
@@ -65,8 +73,8 @@ def _get_loader(data_set, N, episodes_multiplier=1):
 
 def main(args):
     print("Loading dataset...")
-    vocab = read_vocab(args.vocab)
-    X_train, y_train = read_data_set(args.training_set, vocab)
+    vocab = get_vocab(args.embeddings, args.vocab)
+    X_train, y_train = vocab.to_tensors(args.training_set)
 
     # Split training further into train and valid
     X_train, X_valid, y_train, y_valid = train_test_split_tensors(
@@ -77,11 +85,12 @@ def main(args):
     print("Initialising model...")
     model_name = get_model_name(
         distance=args.distance_metric,
-        embeddings='vanilla',
+        embeddings=args.embeddings,
         N=args.N,
         k=args.k)
     model = MatchingNetwork(
         model_name,
+        vocab,
         fce=True,
         processing_steps=args.processing_steps,
         distance_metric=args.distance_metric)

bin/vocab.py

@@ -4,7 +4,7 @@
 
 from argparse import ArgumentParser
 
-from src.data import generate_vocab, store_vocab
+from src.vocab import VanillaVocab
 
 parser = ArgumentParser()
 parser.add_argument("input", help="Path to the input CSV data set")
@@ -13,10 +13,10 @@
 
 def main(args):
     print("Generating vocab...")
-    vocab = generate_vocab(args.input)
+    vocab = VanillaVocab.generate_vocab(args.input)
 
     print("Storing vocab...")
-    store_vocab(vocab, args.output)
+    vocab.save(args.output)
 
     print(f"Stored vocab of size {len(vocab)} at {args.output}")
 

src/data.py

@@ -9,6 +9,9 @@
 from torchtext.data import Field, TabularDataset
 from torchtext.vocab import Vocab
 
+print(
+    "[WARNING] Don't use src.data anymore. Use the Vocab interfaces instead.")
+
 VOCAB_SIZE = 27443
 
 PADDING_TOKEN_INDEX = 1

src/evaluation.py

@@ -6,8 +6,6 @@
 import torch
 import numpy as np
 
-from .data import reverse_tensor
-
 RESULTS_PATH = os.path.join(
     os.path.dirname(os.path.dirname(__file__)), "results")
 
@@ -174,11 +172,11 @@ def _episode_to_text(support_set, targets, labels, target_labels, vocab):
     # First, we need to flatten these...
     N, k, _ = support_set.shape
     flat_support_set = support_set.view(N * k, -1)
-    flat_support_set = reverse_tensor(flat_support_set, vocab)
+    flat_support_set = vocab.to_text(flat_support_set)
     support_set = flat_support_set.reshape(N, k)
 
-    targets = reverse_tensor(targets, vocab)
-    labels = reverse_tensor(labels, vocab)
-    target_labels = reverse_tensor(target_labels, vocab)
+    targets = vocab.to_text(targets)
+    labels = vocab.to_text(labels)
+    target_labels = vocab.to_text(target_labels)
 
     return support_set, targets, labels, target_labels