Create README.md

README.md

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+# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus 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_nsmc.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
+classes = ['Neg', 'Pos']
+
+tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def model_fn(model_path=None):
+    ####
+    # 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)
+    
+    # Download model from the Huggingface hub
+    model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc')   
+    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"][0]
+        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)
+
+        softmax_fn = nn.Softmax(dim=1)
+        softmax_output = softmax_fn(output[0])
+        _, prediction = torch.max(softmax_output, dim=1)
+
+        predicted_class_idx = prediction.item()
+        predicted_class = classes[predicted_class_idx]
+        score = softmax_output[0][predicted_class_idx]
+        logger.info("predicted_class: {}".format(predicted_class))
+
+        prediction_dict = {}
+        prediction_dict["predicted_label"] = predicted_class
+        prediction_dict['score'] = score.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_nsmc import model_fn, input_fn, predict_fn, output_fn
+>>> with open('samples/nsmc.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_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다
+[{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다
+[{inference_nsmc.py:77} INFO - predicted_class: Pos
+[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613}
+[{inference_nsmc.py:77} INFO - predicted_class: Neg
+[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967}
+{"predicted_label": "Pos", "score": 0.9619030952453613}
+{"predicted_label": "Neg", "score": 0.9994170665740967}
+```
+
+### Sample data (samples/nsmc.txt)
+```
+{"text": ["이 영화는 최고의 영화입니다"]}
+{"text": ["최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다"]}
+```
+
+## References
+- KoELECTRA: https://github.com/monologg/KoELECTRA
+- Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc