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Transducens/
xlm-roberta-base-parallel-urls-classifier

Name: xlm-roberta-base-parallel-urls-classifier
Availability: InStock

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Usage

python

import re
import urllib.parse

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import nltk.tokenize
import torch

preprocess_tokenizer_regex = r'[^\W_0-9]+|[^\w\s]+|_+|\s+|[0-9]+' # Similar to wordpunct_tokenize
preprocess_tokenizer = nltk.tokenize.RegexpTokenizer(preprocess_tokenizer_regex).tokenize

def preprocess_url(url):
    protocol_idx = url.find("://")
    protocol_idx = (protocol_idx + 3) if protocol_idx != -1 else 0
    url = url.rstrip('/')[protocol_idx:]
    url = urllib.parse.unquote(url, errors="backslashreplace")

    # Remove blanks
    url = re.sub(r'\s+', ' ', url)
    url = re.sub(r'^\s+|\s+$', '', url)

    # Tokenize
    url = ' '.join(preprocess_tokenizer(url))

    return url

tokenizer = AutoTokenizer.from_pretrained("Transducens/xlm-roberta-base-parallel-urls-classifier")
model = AutoModelForSequenceClassification.from_pretrained("Transducens/xlm-roberta-base-parallel-urls-classifier")

# prepare input
url1 = preprocess_url("https://web.ua.es/en/culture.html")
url2 = preprocess_url("https://web.ua.es/es/cultura.html")
urls = f"{url1}{tokenizer.sep_token}{url2}"
encoded_input = tokenizer(urls, add_special_tokens=True, truncation=True, padding="longest",
                          return_attention_mask=True, return_tensors="pt", max_length=256)

# forward pass
output = model(encoded_input["input_ids"], encoded_input["attention_mask"])

# obtain probability
probability = torch.sigmoid(output["logits"]).cpu().squeeze().item()

print(probability)