---
base_model:
- microsoft/markuplm-base
library_name: transformers
pipeline_tag: token-classification
tags:
- html
- parser
- web-crawler
- news
- crawler
---

This is fine-tuned PoC of [markuplm-base](https://huggingface.co/microsoft/markuplm-base) model for parsing news attributes from web-pages: author, pulication date, content and etc.

**Inference example**

Code accepts URL as input, loads a web-page and returns the json with extracted data (author, publication date, title and content).

```python

id2label = {0: "none", 1:"title", 2:"content", 3:"author", 4: "date", 5: "header", 6: "footer", 7: "rail", 8: "advertisement", 9: "navigation"}

def eval(url):
    current_dir = os.path.dirname(os.path.abspath(__file__))

    model_folder = os.path.join(current_dir, 'models')  # models folder is in the repository root
    model_name = 'OxMarkupLM.pt'

    processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
    processor.parse_html = False

    model_path = os.path.join(model_folder, model_name)

    model = MarkupLMForTokenClassification.from_pretrained(
        model_path, id2label=labels.id2label, label2id=labels.label2id
    )

    html = utils.clean_html(utils.get_html_content(url))
    data = [utils.extract_nodes_and_feautures(html)]
    example = utils.split_sliding_data(data, 10, 0)

    title, author, date, content = [], [], [], []
    for splited in example:
        nodes, xpaths = splited['nodes'], splited['xpaths']
        encoding = processor(
            nodes=nodes, xpaths=xpaths, return_offsets_mapping=True,
            padding="max_length", truncation=True, max_length=512, return_tensors="pt"
        )
        offset_mapping = encoding.pop("offset_mapping")
        with torch.no_grad():
            logits = model(**encoding).logits

        predictions = logits.argmax(-1)
        processed_words = []

        for pred_id, word_id, offset in zip(predictions[0].tolist(), encoding.word_ids(0), offset_mapping[0].tolist()):
            if word_id is not None and offset[0] == 0:
                if pred_id == 1:
                    title.append(nodes[word_id])
                elif pred_id == 2 and word_id not in processed_words:
                    processed_words.append(word_id)
                    content.append(nodes[word_id])
                elif pred_id == 3:
                    author.append(nodes[word_id])
                elif pred_id == 4:
                    date.append(nodes[word_id])

    title = rank_titles(title, '\n'.join(content))
    return {
        "model_name": model_name,
        "url": url,
        "title": title,
        "author": author,
        "date": date,
        "content": content,
    }
```

**More details**

More details how this model was finetuned you can find here [Building LLM crawler](https://medium.com/@olga.rondareva/building-llm-crawler-cb73f46c73ad)