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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: gliner |
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pipeline_tag: token-classification |
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datasets: |
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- gretelai/synthetic_pii_finance_multilingual |
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base_model: |
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- urchade/gliner_small-v2.1 |
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--- |
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# Gravitee GliNER PII Detection 🚀 |
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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. |
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The list of entities is provided in Evaluation results; however, due to the model's nature, it has the capability to identify other entity types as well. |
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Evaluation results: |
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| Entity | Precision | Recall | F1 Score | |
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|---------------------------|-----------|-----------|-----------| |
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| email | 0.937213 | 0.925870 | 0.931507 | |
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| phone_number | 0.898515 | 0.876812 | 0.887531 | |
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| name | 0.929052 | 0.824776 | 0.873814 | |
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| date_of_birth | 0.813953 | 0.937500 | 0.871369 | |
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| date | 0.888942 | 0.839801 | 0.863673 | |
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| location | 0.881579 | 0.829833 | 0.854924 | |
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| company | 0.821222 | 0.873162 | 0.846396 | |
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| ipv4 | 0.791667 | 0.890625 | 0.838235 | |
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| ssn | 0.897959 | 0.785714 | 0.838095 | |
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| bank_routing_number | 0.898305 | 0.746479 | 0.815385 | |
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| driver_license_number | 0.918367 | 0.725806 | 0.810811 | |
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| passport_number | 0.918367 | 0.714286 | 0.803571 | |
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| credit_card_security_code | 0.830986 | 0.756410 | 0.791946 | |
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| time | 0.834297 | 0.674455 | 0.745909 | |
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## Installation |
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To use this model, you must install the GLiNER Python library: |
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``` |
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!pip install gliner |
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``` |
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## Usage |
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Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`. |
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```python |
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from gliner import GLiNER |
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# if you want to use quant model put "model.quant.onnx" in onnx_model_file argument |
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model = GLiNER.from_pretrained( |
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"gravitee-io/gliner-pii-detection", load_onnx_model=True, |
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load_tokenizer=True, onnx_model_file="model.onnx" |
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) |
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text = """ |
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Hey, just a quick update — I talked to David yesterday. |
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He sent over the files from his private email ([email protected]), and we should be careful with his SSN: 123-45-6789. |
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He mentioned his new address is 123 Maple Street in New York. |
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His PC adress is 192.168.1.100. |
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""" |
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labels = ["name", |
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"email", |
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"ssn", |
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"street_address", |
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"date", |
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"ipv4"] |
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entities = model.predict_entities(text, labels) |
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for entity in entities: |
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print(entity["text"], "=>", entity["label"], "=>", entity["score"]) |
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``` |
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``` |
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David => name => 0.9066112041473389 |
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yesterday => date => 0.9482080340385437 |
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[email protected] => email => 0.9911587834358215 |
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123-45-6789 => ssn => 0.8612598180770874 |
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123 Maple Street in New York => street_address => 0.9869663715362549 |
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192.168.1.100 => ipv4 => 0.9810121059417725 |
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``` |
