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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("impresso-project/nel-hipe-multilingual")
model = AutoModelForSeq2SeqLM.from_pretrained(
"impresso-project/nel-hipe-multilingual"
).eval()
def disambiguate_sentences(sentences):
results = []
for sentence in sentences:
outputs = model.generate(
**tokenizer([sentence], return_tensors="pt"),
num_beams=5,
num_return_sequences=5
)
decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)
results.append(decoded)
return results
input_sentences = gr.inputs.Textbox(
lines=5,
label="Input Sentences",
placeholder="Enter your sentence here in the following format: \\ `It is reported in [START] Paris [END], "
"that the opening of the chambers will take place on the 27th January.' \\ "
"This format ensures that the model knows which entities to disambiguate, more exactly the entity should "
"be surrounded by `[START]` and `[END]`.",
)
output_predictions = gr.outputs.Textbox(label="Predictions")
gr.Interface(
fn=disambiguate_sentences,
inputs=input_sentences,
outputs=output_predictions,
title="NEL Disambiguation",
).launch()
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