add train example in readme

README.md

@@ -133,3 +133,38 @@ op = ops.sentence_embedding.transformers().get_op()
 full_list = op.supported_model_names()
 onnx_list = op.supported_model_names(format='onnx')
 ```
+
+## Fine-tune
+### Requirement
+If you want to train this operator, besides dependency in requirements.txt, you need install these dependencies.
+```python
+! python -m pip install datasets evaluate scikit-learn
+```
+### Get started
+
+Simply speaking, you only need to construct an op instance and pass in some configurations to train the specified task.
+```python
+import towhee
+
+bert_op = towhee.ops.sentence_embedding.transformers(model_name='bert-base-uncased').get_op()
+data_args = {
+    'dataset_name': 'wikitext',
+    'dataset_config_name': 'wikitext-2-raw-v1',
+}
+training_args = {
+    'num_train_epochs': 3, # you can add epoch number to get a better metric.
+    'per_device_train_batch_size': 8,
+    'per_device_eval_batch_size': 8,
+    'do_train': True,
+    'do_eval': True,
+    'output_dir': './tmp/test-mlm',
+    'overwrite_output_dir': True
+}
+bert_op.train(task='mlm', data_args=data_args, training_args=training_args)
+
+```
+For more infos, refer to the [examples](https://github.com/towhee-io/examples/tree/main/fine_tune/6_train_language_modeling_tasks).
+
+### Dive deep and customize your training
+You can change the [training script](https://towhee.io/text-embedding/transformers/src/branch/main/train_clm_with_hf_trainer.py) in your customer way. 
+Or your can refer to the original [hugging face transformers training examples](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling).