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--- |
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license: gemma |
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library_name: transformers |
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pipeline_tag: text-generation |
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extra_gated_heading: Access Gemma on Hugging Face |
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extra_gated_prompt: >- |
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To access Gemma on Hugging Face, you’re required to review and agree to |
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Google’s usage license. To do this, please ensure you’re logged in to Hugging |
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Face and click below. Requests are processed immediately. |
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extra_gated_button_content: Acknowledge license |
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tags: |
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- conversational |
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base_model: |
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- google/gemma-3-270m-it |
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--- |
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# gemma3-270m-it-sms-verification_code_extraction |
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#### SMS에서 인증번호를 추출 하는 gemma3 SFT 모델 |
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- 모바일 앱에서 사용하기 위한 Quantization 모델은 아래 참조. |
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- https://huggingface.co/sg2023/gemma3-270m-it-sms-verification_code_extraction-int8-tflite |
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- https://huggingface.co/sg2023/gemma3-270m-it-sms-verification_code_extraction-fp16-tflite |
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- Input / Output |
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- `"본인인증번호는 315611 입니다. 정확히 입력해주세요."` -> `315611` |
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- `"안녕하세요"` -> `0` |
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```python |
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from transformers import Gemma3ForCausalLM, AutoTokenizer |
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repo_id = "sg2023/gemma3-270m-it-sms-verification_code_extraction" |
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tokenizer = AutoTokenizer.from_pretrained(repo_id) |
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model = Gemma3ForCausalLM.from_pretrained(repo_id) |
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model.eval() |
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prompt = "본인인증번호는 315611 입니다. 정확히 입력해주세요." |
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request_template = [{"role": "user", "content": prompt}] |
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response_template = tokenizer.apply_chat_template(request_template, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(response_template, return_tensors="pt").to(model.device) |
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input_ids = inputs["input_ids"][0] # Tensor shape: (seq_len,) |
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input_len = input_ids.shape[0] |
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outputs = model.generate(**inputs, max_new_tokens=64)[0] |
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outputs = outputs[input_len:] |
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outputs = tokenizer.decode(outputs, skip_special_tokens=True) |
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outputs = outputs.strip() |
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print(outputs) # 315611 |
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``` |
