htNghiaaa/phobert-vietnamese-recommendation-1

PhoBERT Vietnamese News Recommendation Model

This model is fine-tuned from vinai/phobert-base for Vietnamese news recommendation using contrastive learning on news categories.

Dataset Structure

The model was trained on a Vietnamese news dataset with the following columns:

• URL: Article URL
• Title: Article title
• Summary: Article summary
• Contents: Full article content
• Date: Publication date
• Author(s): Article author(s)
• Category: Article category (used as labels)
• Tags: Related tags

Model Details

• Base Model: vinai/phobert-base
• Task: Content-Based News Recommendation
• Language: Vietnamese
• Method: Classification-based contrastive learning + FAISS similarity search
• Input: Title + Summary (concatenated)

Usage

Load Model and Generate Recommendations

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import faiss
import numpy as np
import pickle
from sklearn.preprocessing import normalize
from huggingface_hub import hf_hub_download

# Load model and tokenizer
model_name = "htNghiaaa/phobert-vietnamese-recommendation-1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Load FAISS index and metadata
index_path = hf_hub_download(repo_id=model_name, filename="faiss_index.index")
metadata_path = hf_hub_download(repo_id=model_name, filename="metadata.pkl")

index = faiss.read_index(index_path)
with open(metadata_path, "rb") as f:
    metadata = pickle.load(f)

# Function to get embeddings
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
    with torch.no_grad():
        outputs = model.roberta(**inputs)
        embedding = outputs.last_hidden_state.mean(dim=1).numpy()
    return normalize(embedding, norm='l2').astype('float32')

# Function to get recommendations
def get_recommendations(query_text, top_k=5):
    query_embedding = get_embedding(query_text)
    similarities, indices = index.search(query_embedding, top_k)
    
    results = []
    for sim, idx in zip(similarities[0], indices[0]):
        results.append({
            'title': metadata['titles'][idx],
            'summary': metadata['summaries'][idx],
            'category': metadata['categories'][idx],
            'tags': metadata['tags'][idx],
            'url': metadata['urls'][idx],
            'similarity': float(sim)
        })
    return results

# Example
recommendations = get_recommendations("Bóng đá Việt Nam")
for rec in recommendations:
    print(f"[{rec['category']}] {rec['title']} (sim: {rec['similarity']:.4f})")

Training Details

• Model: vinai/phobert-base
• Training Method: Classification on news categories
• Embedding Method: Mean pooling of last hidden state
• Similarity Metric: Cosine similarity (via FAISS)
• GPU: NVIDIA P100/T4
• Batch Size: 16
• Epochs: 3

Files Included

• Model weights and config
• Tokenizer files
• faiss_index.index: FAISS index for similarity search
• embeddings.npy: Pre-computed embeddings
• metadata.pkl: Article metadata (urls, titles, summaries, categories, tags, authors, dates)
• label_encoder.pkl: Category label encoder