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from statistics import mean
import random
import torch
from transformers import BertModel, BertTokenizerFast
import numpy as np
import torch.nn.functional as F
import gradio as gr
tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
model = BertModel.from_pretrained("setu4993/LaBSE")
model = model.eval()
def embed(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = model(**inputs)
return outputs.pooler_output
def similarity(embeddings_1, embeddings_2):
normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
return torch.matmul(
normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
)
def semantic_sim(sentence1, sentence2):
em1 = embed(sentence1, tokenizer, model)
em2 = embed(sentence2, tokenizer, model)
sim = int(float(similarity(em1, em2)*5))
out = ""
if sim == 5:
out = "Equivalent"
elif sim == 4:
out = "Mostly equivalent, unimportant details differ"
elif sim == 3:
out = "Roughly equivalent, important details differ or are missing"
elif sim == 2:
out = "Not equivalent, but share some details"
elif sim == 1:
out = "Same general topic, but not equivalent"
elif sim == 0:
out = "Completely dissimilar"
return out
iface = gr.Interface(fn=semantic_sim, inputs=["text", "text"], outputs=["text"]).launch()
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