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from transformers import Pipeline |
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import nltk |
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nltk.download("averaged_perceptron_tagger") |
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nltk.download("averaged_perceptron_tagger_eng") |
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import requests |
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def get_wikipedia_page_props(input_str: str): |
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""" |
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Retrieves the QID for a given Wikipedia page name from the specified language Wikipedia. |
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If the request fails, it falls back to using the OpenRefine Wikidata API. |
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Args: |
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input_str (str): The input string in the format "page_name >> language". |
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Returns: |
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str: The QID or "NIL" if the QID is not found. |
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""" |
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try: |
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page_name, language = input_str.split(" >> ") |
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page_name = page_name.strip() |
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language = language.strip() |
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except ValueError: |
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return "Invalid input format. Use 'page_name >> language'." |
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wikipedia_url = f"https://{language}.wikipedia.org/w/api.php" |
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wikipedia_params = { |
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"action": "query", |
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"prop": "pageprops", |
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"format": "json", |
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"titles": page_name, |
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} |
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qid = "NIL" |
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try: |
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response = requests.get(wikipedia_url, params=wikipedia_params) |
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response.raise_for_status() |
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data = response.json() |
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if "pages" in data["query"]: |
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page_id = list(data["query"]["pages"].keys())[0] |
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if "pageprops" in data["query"]["pages"][page_id]: |
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page_props = data["query"]["pages"][page_id]["pageprops"] |
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if "wikibase_item" in page_props: |
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return page_props["wikibase_item"] |
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else: |
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return qid |
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else: |
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return qid |
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except Exception as e: |
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return qid |
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def get_wikipedia_title(qid, language="en"): |
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url = f"https://www.wikidata.org/w/api.php" |
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params = { |
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"action": "wbgetentities", |
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"format": "json", |
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"ids": qid, |
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"props": "sitelinks/urls", |
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"sitefilter": f"{language}wiki", |
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} |
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response = requests.get(url, params=params) |
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data = response.json() |
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try: |
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title = data["entities"][qid]["sitelinks"][f"{language}wiki"]["title"] |
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url = data["entities"][qid]["sitelinks"][f"{language}wiki"]["url"] |
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return title, url |
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except KeyError: |
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return "NIL", "None" |
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class NelPipeline(Pipeline): |
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def _sanitize_parameters(self, **kwargs): |
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preprocess_kwargs = {} |
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if "text" in kwargs: |
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preprocess_kwargs["text"] = kwargs["text"] |
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return preprocess_kwargs, {}, {} |
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def preprocess(self, text, **kwargs): |
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outputs = self.model.generate( |
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**self.tokenizer([text], return_tensors="pt"), |
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num_beams=5, |
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num_return_sequences=5, |
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max_new_tokens=30, |
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) |
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wikipedia_predictons = self.tokenizer.batch_decode( |
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outputs, skip_special_tokens=True |
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) |
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return wikipedia_predictons |
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def _forward(self, inputs): |
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return inputs |
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def postprocess(self, outputs, **kwargs): |
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""" |
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Postprocess the outputs of the model |
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:param outputs: |
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:param kwargs: |
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:return: |
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""" |
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results = [] |
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for wikipedia_name in outputs: |
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qid = get_wikipedia_page_props(wikipedia_name) |
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title, url = get_wikipedia_title(qid) |
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results.append({"title": title, "qid": qid, "url": url}) |
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return results |
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