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--- |
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library_name: peft |
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base_model: facebook/mcontriever-msmarco |
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language: |
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- ko |
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--- |
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# smartPatent-mContriever-lora |
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The model is fine-tuned on the customed Korean Patent Retrieval system. |
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### Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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Two types of datasets are used as training data: queries automatically generated through GPT-4 and patent titles that are linked to existing patent abstracts. |
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### Usage |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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```python |
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from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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from peft import PeftModel, PeftConfig |
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def get_model(peft_model_name): |
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config = PeftConfig.from_pretrained(peft_model_name) |
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base_model = AutoModel.from_pretrained(config.base_model_name_or_path) |
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model = PeftModel.from_pretrained(base_model, peft_model_name) |
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model = model.merge_and_unload() |
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model.eval() |
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return model |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained('facebook/mcontriever-msmarco') |
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model = get_model('hanseokOh/smartPatent-mContriever-lora') |
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``` |
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### Info |
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- **Developed by:** hanseokOh |
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- **Model type:** information retriever |
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- **Language(s) (NLP):** Korean |
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- **Finetuned from model [optional]:** mContriever-msmarco |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/hanseokOh/PatentSearch |
