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from transformers import pipeline
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
from torch.nn.functional import cosine_similarity
import gradio as gr
def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
def get_similarity(sentence1, sentence2):
input_texts = [sentence1, sentence2]
# Tokenize and compute embeddings
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict["attention_mask"])
similarity = cosine_similarity(embeddings[0].unsqueeze(0), embeddings[1].unsqueeze(0))
similarity = round(similarity.item(), 4)
return similarity
checkpoint = "intfloat/multilingual-e5-large"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModel.from_pretrained(checkpoint)
demo = gr.Blocks(theme='sudeepshouche/minimalist')
with demo:
gr.Markdown("# MAGIC Sentence Similarity")
gr.Markdown("### How to use:")
gr.Markdown("- Enter Passage 1 and Passage 2, then press Submit")
gr.Markdown("Model: https://huggingface.co/intfloat/multilingual-e5-large (Multilingual: 94 languages)")
with gr.Row():
p_txt1 = gr.Textbox(placeholder="Enter passage 1", label="Passage 1", lines=3, scale=2)
p_txt2 = gr.Textbox(placeholder="Enter passage 2", label="Passage 2", lines=3, scale=2)
o_txt = gr.Textbox(placeholder="Similarity score", lines=1, interactive=False, label="Similarity score (0-1)", scale=1)
submit = gr.Button("Submit")
submit.click(
get_similarity,
[p_txt1, p_txt2],
o_txt
)
demo.launch() |