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from datasets import load_dataset, load_from_disk, Dataset
import os
from transformers import AutoTokenizer, AutoModel
import torch
import pandas as pd
datasetPath = "dataset/github.ds"
if os.path.exists(datasetPath):
issues_dataset = load_from_disk(datasetPath)
else:
issues_dataset = load_dataset("lewtun/github-issues", split="train")
issues_dataset.save_to_disk(datasetPath)
issues_dataset = issues_dataset.filter(
lambda x: (x["is_pull_request"] == False and len(x["comments"]) > 0)
)
columns = issues_dataset.column_names
columns_to_keep = ["title", "body", "html_url", "comments"]
columns_to_remove = set(columns_to_keep).symmetric_difference(columns)
issues_dataset = issues_dataset.remove_columns(columns_to_remove)
issues_dataset.set_format("pandas")
df = issues_dataset[:]
comments_df = df.explode("comments", ignore_index=True)
comments_dataset = Dataset.from_pandas(comments_df)
comments_dataset = comments_dataset.map(
lambda x: {"comment_length": len(x["comments"].split())}
)
comments_dataset = comments_dataset.filter(lambda x: x["comment_length"] > 15)
def concatenate_text(examples):
return {
"text": examples["title"]
+ " \n "
+ examples["body"]
+ " \n "
+ examples["comments"]
}
comments_dataset = comments_dataset.map(concatenate_text)
########################
model_ckpt = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
model = AutoModel.from_pretrained(model_ckpt)
device = torch.device("cuda")
model.to(device)
def cls_pooling(model_output):
return model_output.last_hidden_state[:, 0]
def get_embeddings(text_list):
encoded_input = tokenizer(
text_list, padding=True, truncation=True, return_tensors="pt"
)
encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
model_output = model(**encoded_input)
return cls_pooling(model_output)
embedding = get_embeddings(comments_dataset["text"][0])
embeddings_dataset = comments_dataset.map(
lambda x: {"embeddings": get_embeddings(x["text"]).detach().cpu().numpy()[0]}
)
embeddings_dataset.add_faiss_index(column="embeddings")
# embeddings_dataset.save_to_disk("dataset/embeddings")
question = "How can I load a dataset offline?"
question_embedding = get_embeddings([question]).cpu().detach().numpy()
scores, samples = embeddings_dataset.get_nearest_examples(
"embeddings", question_embedding, k=5
)
samples_df = pd.DataFrame.from_dict(samples)
samples_df["scores"] = scores
samples_df.sort_values("scores", ascending=False, inplace=True)
for _, row in samples_df.iterrows():
print(f"COMMENT: {row.comments}")
print(f"SCORE: {row.scores}")
print(f"TITLE: {row.title}")
print(f"URL: {row.html_url}")
print("=" * 50)
print()
print(issues_dataset) |