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app.py

@@ -15,7 +15,7 @@ from src.task.inference.classify import TextClassifier
 MODEL_PATH = Path("checkpoints/tiny_text_generator.pt")
 
 CLASSIFIER_MODEL_PATH = Path(
-    "checkpoints/news-classifier.pt"
+    "checkpoints/tiny-text-classifier.pt"
     )
 
 CLASSIFIER_TOKENIZER_PATH = Path(
@@ -70,12 +70,14 @@ classifier_tokenizer = CharacterTokenizer.load(
     CLASSIFIER_TOKENIZER_PATH
     )
 
+config.context_length = 384
 classifier_model = load_classifier_model(
     checkpoint_path=CLASSIFIER_MODEL_PATH,
     config=config,
     vocab_size=classifier_tokenizer.vocab_size,
     )
 
+config.context_length = 64
 text_classifier = TextClassifier(
     model=classifier_model,
     tokenizer=classifier_tokenizer,

checkpoints/tiny-text-classifier.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc414481ecb68c0bd4d56204b5496abaf70690eab6a316d6bab27ef3e5978c26
+size 792521

scripts/predict_class.py

@@ -11,7 +11,7 @@ from src.infra.dataset.util import build_text
 
 # Runtime configs
 # corpus = "data/tiny_shakespear.txt"
-checkpoint = "checkpoints/news-classifier.pt"
+checkpoint = "checkpoints/tiny-text-classifier.pt"
 
 # CLASS_TOKENIZER_PATH = "ag_news_tokenizer.json"
 CLASS_TOKENIZER_PATH = Path("checkpoints/ag_news_tokenizer.json")
@@ -27,13 +27,14 @@ CLASS_NAMES = [
     ]
 
 # Conf
-config = ModelConfig()
+model_config = ModelConfig()
+model_config.context_length = 384
 
 
 # Load model/checkpoint from file 
 model = load_classifier_model(
     checkpoint_path=checkpoint,
-    config=config,
+    config=model_config,
     vocab_size=tokenizer.vocab_size,
 )
 
@@ -42,7 +43,7 @@ model = load_classifier_model(
 text_classifier = TextClassifier(
     model=model,
     tokenizer=tokenizer,
-    context_length=config.context_length,
+    context_length=model_config.context_length,
     )
 
 

scripts/train_classifier.py

@@ -13,35 +13,39 @@ from src.task.model import TinyTransformerLM, TinyClassifier
 from src.task.training.classification import train
 import pandas as pd
 import numpy as np
+from src.task.training.util import TrainingLogger, count_parameters, separator
 
 
 
-def separator(title: str) -> None:
-    print("\n" + "=" * 60)
-    print(title)
-    print("=" * 60)
-
-separator("Helpers")
-
+# Custom Logger
+logger = TrainingLogger(
+    "logs/text_classifier.log"
+    )
 
 
 # Configs
 separator("Configuration")
 
-# torch.manual_seed(42)
-
+torch.manual_seed(42)
+# Configs
 model_config = ModelConfig()
 training_config = TrainingConfig()
 
-print(model_config)
-print(training_config)
+# Overrides
+model_config.context_length = 384
+training_config.num_steps = 10 
+training_config.eval_interval = 1
+training_config.num_train_samples = None
+
+logger.log(model_config)
+logger.log(training_config)
 
 # Load the corpus
 separator("Corpus")
 
 TRAIN_PATH = Path("data/ag-news/train.csv")
 TEST_PATH = Path("data/ag-news/test.csv")
-checkpoint = "news-classifier.pt"
+checkpoint = "tiny-text-classifier.pt"
 
 if not TRAIN_PATH.exists():
     raise FileNotFoundError(f"Training file not found: {TRAIN_PATH}")

scripts/train_generator.py

@@ -10,16 +10,12 @@ from src.task.config import ModelConfig,TrainingConfig
 
 from src.task.model import TinyTransformerLM
 from src.task.training.autoregressive import train, evaluate
-from src.task.training.util import TrainingLogger, count_parameters
+from src.task.training.util import TrainingLogger, count_parameters, separator
 
-def separator(title: str) -> None:
-    print("\n" + "=" * 60)
-    print(title)
-    print("=" * 60)
 
-# Take stok
+# Custom Logger
 logger = TrainingLogger(
-    "logs/text_generator.txt"
+    "logs/text_generator.log"
     )
 
 
@@ -32,10 +28,10 @@ separator("Configuration")
 
 torch.manual_seed(42)
 
-# Generator
+# Configs
 model_config = ModelConfig()
 training_config = TrainingConfig()
-
+# Overrides
 training_config.num_steps = 10000 
 training_config.eval_interval = 1000
 

src/task/training/util.py

@@ -31,4 +31,11 @@ def count_parameters(model):
     print(
         f"Parameters: "
         f"{count_parameters(model):,}"
-        )
+        )
+
+
+
+def separator(title: str) -> None:
+    print("\n" + "=" * 60)
+    print(title)
+    print("=" * 60)