add how to use

README.md

@@ -32,6 +32,57 @@ More information needed
 
 More information needed
 
+## How to use
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+
+raw_datasets = load_dataset("glue", "sst2")
+checkpoint = "ChiJuiChen/Bert-Lab4"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+
+def tokenize_function(example):
+    return tokenizer(example["sentence"], truncation=True)
+
+
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+
+small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(100))
+small_eval_dataset = tokenized_datasets["validation"].shuffle(seed=42).select(range(100))
+
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(output_dir="ChiJuiChen/Bert-Lab4",
+                                  evaluation_strategy="epoch",
+                                  hub_model_id="ChiJuiChen/Bert-Lab4")
+
+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
+
+from transformers import Trainer
+trainer = Trainer(
+    model,
+    training_args,
+    train_dataset=small_train_dataset, # if using cpu
+    eval_dataset=small_eval_dataset, # if using cpu
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+# Evaluation
+predictions = trainer.predict(small_eval_dataset)
+print(predictions.predictions.shape, predictions.label_ids.shape)
+preds = np.argmax(predictions.predictions, axis=-1)
+
+import evaluate
+metric = evaluate.load("glue", "sst2")
+metric.compute(predictions=preds, references=predictions.label_ids)
+```
 ## Training procedure
 
 ### Training hyperparameters