Update README.md

README.md

@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-sst2
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on SST-2 dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.6081
 - Accuracy: 0.64
@@ -30,9 +30,61 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+[SST-2 dataset](https://huggingface.co/datasets/gimmaru/glue-sst2)
+We randomly select 100 training data and 100 evaluation data.
+
+## How to use
+
+```
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+
+raw_datasets = load_dataset("glue", "sst2")
+checkpoint = "zhuchi76/bert-finetuned-sst2"
+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="bert-finetuned-sst2",
+                                  evaluation_strategy="epoch",
+                                  hub_model_id="zhuchi76/bert-finetuned-sst2")
+
+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