diff --git a/transformers/Dockerfile b/transformers/Dockerfile
new file mode 100644
index 00000000..66088a72
--- /dev/null
+++ b/transformers/Dockerfile
@@ -0,0 +1,7 @@
+FROM python:3.11
+
+RUN pip install wandb==0.15.5 transformers==4.27.1 datasets torch
+
+COPY train.py .
+
+ENTRYPOINT ["python", "train.py"]
diff --git a/transformers/train.py b/transformers/train.py
new file mode 100644
index 00000000..378358f1
--- /dev/null
+++ b/transformers/train.py
@@ -0,0 +1,72 @@
+from transformers import Trainer, TrainingArguments
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
+import wandb
+import os
+
+
+os.environ["WANDB_LOG_MODEL"] = "checkpoint"
+with wandb.init(entity="gong-demo", project="transformers") as run:
+    
+    # Load the dataset
+    dataset = load_dataset("imdb")
+    
+    # Load the tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", fast=True)
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)  # Binary classification example for IMDB dataset
+    
+    resume = False
+    if os.environ.get("WANDB_RESUME") == "must":
+        # check for an existing checkpoint artifact
+        my_checkpoint_name = f"checkpoint-{run.id}:latest"
+        try:
+            my_checkpoint_artifact = run.use_artifact(my_checkpoint_name)
+            # Download checkpoint to a folder and return the path
+            checkpoint_dir = my_checkpoint_artifact.download()
+            resume = True
+        except Exception as e:
+            print("No previous checkpoint found for run {run.name}")
+
+
+    train_fraction = 0.02  
+    test_fraction = 0.01
+
+    train_dataset = dataset["train"].select(range(int(len(dataset["train"]) * train_fraction)))
+    test_dataset = dataset["test"].select(range(int(len(dataset["test"]) * test_fraction)))
+
+    # Tokenize the datasets
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", fast=True)
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+    tokenized_train_dataset = train_dataset.map(tokenize_function, batched=True)
+    tokenized_test_dataset = test_dataset.map(tokenize_function, batched=True)
+
+    # Define the training arguments
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=50,
+        per_device_train_batch_size=32,
+        per_device_eval_batch_size=128,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        logging_dir="./logs",
+        report_to="wandb",
+    )
+
+    # Define the trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_train_dataset,
+        eval_dataset=tokenized_test_dataset,
+        tokenizer=tokenizer,
+    )
+
+    # Train the model
+    if resume:
+        trainer.train(resume_from_checkpoint=checkpoint_dir)
+    else:
+        trainer.train()
+
+wandb.finish()