Update README.md

README.md

@@ -27,24 +27,67 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# results
+# Bert Tiny for SST2
 
 This model is a fine-tuned version of [M-FAC/bert-tiny-finetuned-sst2](https://huggingface.co/M-FAC/bert-tiny-finetuned-sst2) on the sst2 dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.4771
 - Accuracy: 0.8280
 
-## Model description
+## Usage Example
+
+```python
+  from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding
+  import datasets
+
+  model = BertForSequenceClassification.from_pretrained('VityaVitalich/bert-tiny-sst2')
+  tokenizer = BertTokenizer.from_pretrained('M-FAC/bert-tiny-finetuned-sst2')
+
+  def create_data(tokenizer):
+    train_set = datasets.load_dataset('sst2', split='train').remove_columns(['idx'])
+    val_set = datasets.load_dataset('sst2', split='validation').remove_columns(['idx'])
+
+    def tokenize_func(examples):
+        return tokenizer(examples["sentence"], max_length=128, padding='max_length', truncation=True)
+
+    encoded_dataset_train = train_set.map(tokenize_func, batched=True)
+    encoded_dataset_test = val_set.map(tokenize_func, batched=True)
+    data_collator = DataCollatorWithPadding(tokenizer)
+    return encoded_dataset_train, encoded_dataset_test, data_collator
+
+  encoded_dataset_train, encoded_dataset_test, data_collator = create_data(tokenizer)
+
+  training_args = TrainingArguments(
+      output_dir='./results',
+      learning_rate=3e-5,
+      per_device_train_batch_size=128,
+      per_device_eval_batch_size=128,
+      load_best_model_at_end=True,
+      num_train_epochs=5,
+      weight_decay=0.1,
+      fp16=True,
+      fp16_full_eval=True,
+      evaluation_strategy="epoch",
+      seed=42,
+      save_strategy = "epoch",
+      save_total_limit=5,
+      logging_strategy="epoch",
+      report_to="all",
+  )
+  
+  
+  trainer = Trainer(
+      model=model,
+      args=training_args,
+      train_dataset=encoded_dataset_train,
+      eval_dataset=encoded_dataset_test,
+      data_collator=data_collator,
+      compute_metrics=compute_metrics,
+  )
+
+  trainer.evaluate(encoded_dataset_test)
+```
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
 
 ## Training procedure