lppier · December 27, 2020 06:46 · Dec 27, 2020
diff --git a/HF_CustomSentimentClassifier.py b/HF_CustomSentimentClassifier.py
@@ -0,0 +1,114 @@
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+from transformers import DistilBertTokenizerFast
+import torch
+from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
+import torch.nn.functional as F
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+# IMDB Dataset can be found here
+# wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
+# tar -xf aclImdb_v1.tar.gz
+
+
+def compute_metrics(pred):
+    labels = pred.label_ids
+    preds = pred.predictions.argmax(-1)
+    precision, recall, f1, _ = precision_recall_fscore_support(
+        labels, preds, average="binary"
+    )
+    acc = accuracy_score(labels, preds)
+    return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}
+
+
+def read_imdb_split(split_dir):
+    split_dir = Path(split_dir)
+    texts = []
+    labels = []
+    for label_dir in ["pos", "neg"]:
+        for text_file in (split_dir / label_dir).iterdir():
+            texts.append(text_file.read_text(encoding="utf8"))
+            labels.append(0 if label_dir is "neg" else 1)
+
+    return texts, labels
+
+
+train_texts, train_labels = read_imdb_split("data/aclImdb/train")
+test_texts, test_labels = read_imdb_split("data/aclImdb/test")
+
+# Further split training set to get a validation set
+train_texts, val_texts, train_labels, val_labels = train_test_split(
+    train_texts, train_labels, test_size=0.1
+)
+
+# Get BERT Tokens
+tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
+train_encodings = tokenizer(train_texts, truncation=True, padding=True)
+val_encodings = tokenizer(val_texts, truncation=True, padding=True)
+test_encodings = tokenizer(test_texts, truncation=True, padding=True)
+
+
+class IMDbDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+        item["labels"] = torch.tensor(self.labels[idx])
+        return item
+
+    def __len__(self):
+        return len(self.labels)
+
+
+train_dataset = IMDbDataset(train_encodings, train_labels)
+val_dataset = IMDbDataset(val_encodings, val_labels)
+test_dataset = IMDbDataset(test_encodings, test_labels)
+
+training_args = TrainingArguments(
+    output_dir="./results",  # output directory
+    num_train_epochs=1,  # total number of training epochs
+    per_device_train_batch_size=16,  # batch size per device during training
+    per_device_eval_batch_size=64,  # batch size for evaluation
+    warmup_steps=500,  # number of warmup steps for learning rate scheduler
+    weight_decay=0.01,  # strength of weight decay
+    logging_dir="./logs",  # directory for storing logs
+    logging_steps=10,
+)
+
+model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+trainer = Trainer(
+    model=model,  # the instantiated 🤗 Transformers model to be trained
+    args=training_args,  # training arguments, defined above
+    train_dataset=train_dataset,  # training dataset
+    eval_dataset=val_dataset,  # evaluation dataset
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()  # this saves the models in the checkpoints under results folder
+
+# Inference Code
+
+import pandas as pd
+model = DistilBertForSequenceClassification.from_pretrained("./results_old/checkpoint-3500")
+df_labels = pd.read_csv("data/comprehendimdbtest.csv", header=None)
+test_data = df_labels.iloc[:,1].to_list()
+
+predictions = []
+# Need this loop otherwise my cpu memory just loads up
+for text in test_data:
+    encoding = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**encoding)
+    pt_predictions = F.softmax(outputs[0], dim=-1)
+    predictions.append(pt_predictions.argmax(-1).item()) # warning, don't append the tensor too memory intensive!
+    print(pt_predictions)
+
+
+precision, recall, f1, _ = precision_recall_fscore_support(
+    df_labels.iloc[:, 0], predictions, average="binary"
+)
+acc = accuracy_score(df_labels.iloc[:, 0], predictions)
+metrics = {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}
+print(metrics)