README.md

---
license: cc-by-sa-4.0
---
# 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-url2lang")
model = AutoModelForSequenceClassification.from_pretrained("Transducens/xlm-roberta-base-url2lang")

# prepare input
url = preprocess_url("https://es.wikipedia.org/wiki/Halo_3#Matchmaking")
encoded_input = tokenizer(url, 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 lang
probabilities = torch.softmax(output["logits"], dim=1).cpu().squeeze(0)
lang_idx = torch.argmax(probabilities, dim=0).item()
probability = probabilities[lang_idx].item()
lang = model.config.id2lang[str(lang_idx)]

print(f"Language (probability): {lang} ({probability})")
```