You can deploy this model and start earning money today!

kienhoang123/
ViCLIP

Name: ViCLIP
Brand: AlphaNeural AI
Rating: 2.5 (6 reviews)

Views

No views yet

ViCLIP - Vietnamese CLIP Text Encoder

This model is a Vietnamese adaptation of CLIP text encoder, trained on Vietnamese data.

Model Description

Text encoder based on PhoBERT
Projection head to align with CLIP embedding space
Optimized for Vietnamese text understanding

Usage

python

from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torch.nn.functional as F

class PhoCLIPTextModel(nn.Module):
    def __init__(self):
        super().__init__()
        # Load text encoder
        self.text_encoder = AutoModel.from_pretrained("kienhoang123/ViCLIP")
        
        # Load text projection head
        state_dict = torch.load(hf_hub_download(repo_id="kienhoang123/ViCLIP", filename="model.pt"))
        self.load_state_dict(state_dict)
    
    def forward(self, input_ids, attention_mask=None):
        # Get text embeddings
        text_outputs = self.text_encoder(
            input_ids=input_ids, 
            attention_mask=attention_mask, 
            return_dict=True
        )
        text_cls = text_outputs.last_hidden_state[:, 0, :]
        text_proj = self.text_proj(text_cls)
        return text_proj

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("kienhoang123/ViCLIP")

# Encode text
text = "This is an example Vietnamese text"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=77, truncation=True)

model = PhoCLIPTextModel()
model.eval()

with torch.no_grad():
    embedding = model(inputs.input_ids, inputs.attention_mask)
    normalized_embedding = F.normalize(embedding, p=2, dim=-1)