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from typing import Dict, Union
from gliner import GLiNER
import gradio as gr
model = GLiNER.from_pretrained("wjbmattingly/gliner-hrd")
examples = [
[
"We discovered a collection of deer bones next to a burial mound. Inside the mound we found the finger of a human and twelve ribs from a separate person. We also found a bat wing and a cow skull. There was even a mummified human head.",
"human remains, animal remains, general remains",
0.3,
True,
]
]
def ner(
text, labels: str, threshold: float, nested_ner: bool
) -> Dict[str, Union[str, int, float]]:
labels = labels.split(",")
return {
"text": text,
"entities": [
{
"entity": entity["label"],
"word": entity["text"],
"start": entity["start"],
"end": entity["end"],
"score": 0,
}
for entity in model.predict_entities(
text, labels, flat_ner=not nested_ner, threshold=threshold
)
],
}
with gr.Blocks(title="GLiNER-M-v2.1") as demo:
gr.Markdown(
"""
# GLiNER-base
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.
## Links
* Model: https://huggingface.co/urchade/gliner_multi-v2.1
* All GLiNER models: https://huggingface.co/models?library=gliner
* Paper: https://arxiv.org/abs/2311.08526
* Repository: https://github.com/urchade/GLiNER
"""
)
input_text = gr.Textbox(
value=examples[0][0], label="Text input", placeholder="Enter your text here"
)
with gr.Row() as row:
labels = gr.Textbox(
value=examples[0][1],
label="Labels",
placeholder="Enter your labels here (comma separated)",
scale=2,
)
threshold = gr.Slider(
0,
1,
value=0.3,
step=0.01,
label="Threshold",
info="Lower the threshold to increase how many entities get predicted.",
scale=1,
)
nested_ner = gr.Checkbox(
value=examples[0][2],
label="Nested NER",
info="Allow for nested NER?",
scale=0,
)
output = gr.HighlightedText(label="Predicted Entities")
submit_btn = gr.Button("Submit")
examples = gr.Examples(
examples,
fn=ner,
inputs=[input_text, labels, threshold, nested_ner],
outputs=output,
cache_examples=True,
)
# Submitting
input_text.submit(
fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
)
labels.submit(
fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
)
threshold.release(
fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
)
submit_btn.click(
fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
)
nested_ner.change(
fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
)
demo.queue()
demo.launch(debug=True)
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