Split data into chunks and save model

1 files changed, 35 insertions, 7 deletions

diff --git a/train.py b/train.py
index bbdc54a..9d38f44 100644
--- a/train.py
+++ b/train.py
@@ -1,21 +1,49 @@
 from argparse import ArgumentParser
+from itertools import chain
 
 from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, default_data_collator
 
 
 parser = ArgumentParser()
 parser.add_argument('-i', '--input', default='data',
                     help='training data input file')
+parser.add_argument('-o', '--output', default='model',
+                    help='output directory for trained model')
 args = parser.parse_args()
 
 
+# Load and tokenize dataset
 raw_dataset = load_dataset('text', data_files={'train': args.input}, keep_linebreaks=True)
-
-tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
-tokenized_dataset = raw_dataset.map(lambda examples : tokenizer(examples['text']), batched=True, remove_columns=raw_dataset["train"].column_names)
-
+tokenizer = AutoTokenizer.from_pretrained('distilgpt2', use_fast=True)
+tokenized_dataset = raw_dataset.map(lambda examples : tokenizer(examples['text']), batched=True, remove_columns='text')
+
+
+# Generate chunks of block_size
+block_size = tokenizer.model_max_length
+
+# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
+def group_texts(examples):
+    # Concatenate all texts.
+    concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+    total_length = len(concatenated_examples[list(examples.keys())[0]])
+    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+    # customize this part to your needs.
+    if total_length >= block_size:
+        total_length = (total_length // block_size) * block_size
+    # Split by chunks of max_len.
+    result = {
+        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+        for k, t in concatenated_examples.items()
+    }
+    result["labels"] = result["input_ids"].copy()
+    return result
+
+lm_dataset = tokenized_dataset.map(group_texts, batched=True)
+
+
+# Create and train the model
 model = AutoModelForCausalLM.from_pretrained('distilgpt2')
-
-trainer = Trainer(model=model, train_dataset=tokenized_dataset['train'], tokenizer=tokenizer)
+trainer = Trainer(model=model, train_dataset=lm_dataset['train'], tokenizer=tokenizer, data_collator=default_data_collator)
 trainer.train()
+trainer.save_model(args.output)