Spaces:
Sleeping
Sleeping
File size: 5,212 Bytes
a613a45 428c34f a613a45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
import gradio as gr
from transformers import pipeline
from typing import Dict, Union
from gliner import GLiNER
model = GLiNER.from_pretrained("urchade/gliner_multi-v2.1") # numind/NuNER_Zero
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
css = """
h1 {
text-align: center;
display:block;
}
"""
#define a function to process your input and output
def zero_shot(doc, candidates):
given_labels = candidates.split(", ")
dictionary = classifier(doc, given_labels)
labels = dictionary['labels']
scores = dictionary['scores']
return dict(zip(labels, scores))
examples_text = [
[
"Pasar saham ngalaman panurunan nu signifikan akibat kateupastian global.",
"ékonomi, pulitik, bisnis, kauangan, téknologi"
],
[
"I am very happy today but suddenly sad because of the recent news.",
"positive, negative, neutral"
],
[
"I just received the best news ever! I got the job I always wanted!",
"joy, sadness, anger, surprise, fear, disgust"
],
]
examples_ner = [
[
"Pada tahun 1945, Indonesia memproklamasikan kemerdekaannya dari penjajahan Belanda. Proklamasi tersebut dibacakan oleh Soekarno dan Mohammad Hatta di Jakarta.",
"tahun, negara, tokoh, lokasi",
0.3
],
[
"Mount Everest is the highest mountain above sea level, located in the Himalayas. It stands at 8,848 meters (29,029 ft) and attracts many climbers.",
"location, measurement, person",
0.3
],
[
"Perusahaan teknologi raksasa, Google, mbukak kantor cabang anyar ing Jakarta ing wulan Januari 2020 kanggo nggedhekake operasine ing Asia Tenggara",
"perusahaan, lokasi, wulan, taun",
0.3
],
]
def merge_entities(entities):
if not entities:
return []
merged = []
current = entities[0]
for next_entity in entities[1:]:
if next_entity['entity'] == current['entity'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
current['word'] += ' ' + next_entity['word']
current['end'] = next_entity['end']
else:
merged.append(current)
current = next_entity
merged.append(current)
return merged
def ner(
text, labels: str, threshold: float, nested_ner: bool
) -> Dict[str, Union[str, int, float]]:
labels = labels.split(",")
r = {
"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
)
],
}
r["entities"] = merge_entities(r["entities"])
return r
with gr.Blocks(title="Zero-Shot Demo", css=css) as demo: #, theme=gr.themes.Soft()
gr.Markdown(
"""
# Zero-Shot Model Demo
"""
)
#create input and output objects
with gr.Tab("Zero-Shot Text Classification"):
gr.Markdown(
"""
## Zero-Shot Text Classification
"""
)
input1 = gr.Textbox(label="Text", value=examples_text[0][0])
input2 = gr.Textbox(label="Labels",value=examples_text[0][1])
output = gr.Label(label="Output")
gui = gr.Interface(
# title="Zero-Shot Text Classification",
fn=zero_shot,
inputs=[input1, input2],
outputs=[output]
)
examples = gr.Examples(
examples_text,
fn=zero_shot,
inputs=[input1, input2],
outputs=output,
cache_examples=True,
)
with gr.Tab("Zero-Shot NER"):
gr.Markdown(
"""
## Zero-Shot Named Entity Recognition (NER)
"""
)
input_text = gr.Textbox(
value=examples_ner[0][0], label="Text input", placeholder="Enter your text here", lines=3
)
with gr.Row() as row:
labels = gr.Textbox(
value=examples_ner[0][1],
label="Labels",
placeholder="Enter your labels here (comma separated)",
scale=2,
)
threshold = gr.Slider(
0,
1,
value=examples_ner[0][2],
step=0.01,
label="Threshold",
info="Lower the threshold to increase how many entities get predicted.",
scale=1,
)
output = gr.HighlightedText(label="Predicted Entities")
submit_btn = gr.Button("Submit")
examples = gr.Examples(
examples_ner,
fn=ner,
inputs=[input_text, labels, threshold],
outputs=output,
cache_examples=True,
)
submit_btn.click(
fn=ner, inputs=[input_text, labels, threshold], outputs=output
)
demo.queue()
demo.launch(debug=True) |