wikipedia-assistant / util /query_smoke_test.py
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Duplicate from deepset/wikipedia-assistant
039aebb
import torch
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
def main():
device = ("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained('vblagoje/retribert-base-uncased')
model = AutoModel.from_pretrained('vblagoje/retribert-base-uncased').to(device)
_ = model.eval()
index_file_name = "./data/kilt_wikipedia.faiss"
kilt_wikipedia = load_dataset("kilt_wikipedia", split="full")
columns = ['kilt_id', 'wikipedia_id', 'wikipedia_title', 'text', 'anchors', 'categories',
'wikidata_info', 'history']
min_snippet_length = 20
topk = 21
def articles_to_paragraphs(examples):
ids, titles, sections, texts, start_ps, end_ps, start_cs, end_cs = [], [], [], [], [], [], [], []
for bidx, example in enumerate(examples["text"]):
last_section = ""
for idx, p in enumerate(example["paragraph"]):
if "Section::::" in p:
last_section = p
ids.append(examples["wikipedia_id"][bidx])
titles.append(examples["wikipedia_title"][bidx])
sections.append(last_section)
texts.append(p)
start_ps.append(idx)
end_ps.append(idx)
start_cs.append(0)
end_cs.append(len(p))
return {"wikipedia_id": ids, "title": titles,
"section": sections, "text": texts,
"start_paragraph_id": start_ps, "end_paragraph_id": end_ps,
"start_character": start_cs,
"end_character": end_cs
}
kilt_wikipedia_paragraphs = kilt_wikipedia.map(articles_to_paragraphs, batched=True,
remove_columns=columns,
batch_size=256, cache_file_name=f"./wiki_kilt_paragraphs_full.arrow",
desc="Expanding wiki articles into paragraphs")
# use paragraphs that are not simple fragments or very short sentences
kilt_wikipedia_paragraphs = kilt_wikipedia_paragraphs.filter(lambda x: x["end_character"] > 250)
kilt_wikipedia_paragraphs.load_faiss_index("embeddings", index_file_name, device=0)
def embed_questions_for_retrieval(questions):
query = tokenizer(questions, max_length=128, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
q_reps = model.embed_questions(query["input_ids"].to(device),
query["attention_mask"].to(device)).cpu().type(torch.float)
return q_reps.numpy()
def query_index(question):
question_embedding = embed_questions_for_retrieval([question])
scores, wiki_passages = kilt_wikipedia_paragraphs.get_nearest_examples("embeddings", question_embedding, k=topk)
columns = ['wikipedia_id', 'title', 'text', 'section', 'start_paragraph_id', 'end_paragraph_id', 'start_character','end_character']
retrieved_examples = []
r = list(zip(wiki_passages[k] for k in columns))
for i in range(topk):
retrieved_examples.append({k: v for k, v in zip(columns, [r[j][0][i] for j in range(len(columns))])})
return retrieved_examples
questions = ["What causes the contrails (cirrus aviaticus) behind jets at high altitude? ",
"Why does water heated to a room temeperature feel colder than the air around it?"]
res_list = query_index(questions[0])
res_list = [res for res in res_list if len(res["text"].split()) > min_snippet_length][:int(topk / 3)]
for res in res_list:
print("\n")
print(res)
main()