Create README.md

README.md

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+# Similarity between two sentences (fine tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
+
+## Usage (Amazon SageMaker inference applicable)
+It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
+
+### inference_korsts.py
+
+```python
+import json
+import sys
+import logging
+import torch
+from torch import nn
+from transformers import ElectraConfig
+from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
+
+logging.basicConfig(
+    level=logging.INFO, 
+    format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler(filename='tmp.log'),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger(__name__)
+
+max_seq_length = 128
+tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-korsts")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
+def model_fn(model_path):
+    ####
+    # If you have your own trained model
+    # Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
+    ####    
+    #config = ElectraConfig.from_json_file(f'{model_path}/config.json')
+    #model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
+    model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-korsts')
+    model.to(device)
+    return model
+
+
+def input_fn(input_data, content_type="application/jsonlines"):
+    data_str = input_data.decode("utf-8")
+    jsonlines = data_str.split("\n")
+    transformed_inputs = []
+    
+    for jsonline in jsonlines:
+        text = json.loads(jsonline)["text"]
+        logger.info("input text: {}".format(text))          
+        encode_plus_token = tokenizer.encode_plus(
+            text,
+            max_length=max_seq_length,
+            add_special_tokens=True,
+            return_token_type_ids=False,
+            padding="max_length",
+            return_attention_mask=True,
+            return_tensors="pt",
+            truncation=True,
+        )
+        transformed_inputs.append(encode_plus_token)
+        
+    return transformed_inputs
+
+
+def predict_fn(transformed_inputs, model):
+    predicted_classes = []
+
+    for data in transformed_inputs:
+        data = data.to(device)
+        output = model(**data)
+
+        prediction_dict = {}
+        prediction_dict['score'] = output[0].squeeze().cpu().detach().numpy().tolist()
+
+        jsonline = json.dumps(prediction_dict)
+        logger.info("jsonline: {}".format(jsonline))        
+        predicted_classes.append(jsonline)
+
+    predicted_classes_jsonlines = "\n".join(predicted_classes)
+    return predicted_classes_jsonlines
+
+
+def output_fn(outputs, accept="application/jsonlines"):
+    return outputs, accept
+```    
+
+### test.py 
+```python
+>>> from inference_korsts import model_fn, input_fn, predict_fn, output_fn
+>>> with open('./samples/korsts.txt', mode='rb') as file:
+>>>    model_input_data = file.read()
+
+>>> model = model_fn()
+>>> transformed_inputs = input_fn(model_input_data)
+>>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
+>>> model_outputs = output_fn(predicted_classes_jsonlines)
+>>> print(model_outputs[0])     
+
+[{inference_korsts.py:44} INFO - input text: ['맛있는 라면을 먹고 싶어요', '후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면']
+[{inference_korsts.py:44} INFO - input text: ['뽀로로는 내친구', '머신러닝은 러닝머신이 아닙니다.']
+[{inference_korsts.py:71} INFO - jsonline: {"score": 4.786738872528076}
+[{inference_korsts.py:71} INFO - jsonline: {"score": 0.2319069355726242}
+{"score": 4.786738872528076}
+{"score": 0.2319069355726242}
+```
+
+### Sample data (samples/korsts.txt)
+```
+{"text": ["맛있는 라면을 먹고 싶어요", "후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면"]}
+{"text": ["뽀로로는 내친구", "머신러닝은 러닝머신이 아닙니다."]}
+```
+
+## References
+- KoELECTRA: https://github.com/monologg/KoELECTRA
+- KorNLI and KorSTS: https://github.com/kakaobrain/KorNLUDatasets