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kennethge123/
qwen3-bart-corrector

Name: qwen3-bart-corrector
Availability: InStock

safetensors

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Qwen3 BART Corrector Model

Vec2Text iterative corrector model using BART-base. Works with the inversion model to iteratively refine text reconstructions from Qwen3-Embedding-8B embeddings.

Model Details

Base model: facebook/bart-base
Embedding model: Qwen3-Embedding-8B (4096-dim)
Training data: 1M sentences from ClimbMix corpus
Architecture: Vec2Text embedding inversion (embedding -> text)
Type: Corrector (stage 2+3 of Vec2Text pipeline)
Requires: Inversion model + Qwen3-Embedding-8B for iterative correction

Evaluation Results (200 held-out samples)

Metric	Score
Token F1	0.6837 (+/-0.1693)
BLEU-4	0.2797 (+/-0.2664)
ROUGE-L	0.5701 (+/-0.2118)
Exact Match	7/200 (3.5%)

Example Reconstructions

Original	Reconstructed	Token F1
"This is important because childhood sets the stage for the robustness of the im...	"This is crucial because the childhood sets robust foundations for the immune sy...	0.811
Chapter 8: Made in the USA - A Statement About Quality and Pride

Have you ever ... | Chapter 8: Quality and Quality: The USA Made in America

Have you ever felt that... | 0.594 | | Less than 30% of the 36 million cat owners in the U.S. know that the beautiful s... | Did you know that less than 36% of cat owners in the U.S. know that their cat ca... | 0.560 | | The key ingredients of Turkish meals are meat, vegetables, and legumes.... | Key ingredients of Turkish meals include meat, vegetables, and legumes.... | 0.828 | | The platform will then generate a list of available options with prices, travel ... | The platform will then list available options with a given price, amenities, tra... | 0.775 |

Usage

python

import torch, torch.nn as nn, transformers
from safetensors.torch import load_file

# Load model
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-base")
tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/bart-base")
hidden = model.config.hidden_size

embedding_transform = nn.Sequential(
    nn.Linear(4096, 4096), nn.LayerNorm(4096), nn.Dropout(0.1), nn.GELU(),
    nn.Linear(4096, hidden * 16),
)

# Load weights (download from this repo)
state = load_file("model.safetensors")  # or torch.load("bart_noisy.pt")
et_state = {k.replace("embedding_transform.", ""): v for k, v in state.items() if k.startswith("embedding_transform.")}
embedding_transform.load_state_dict(et_state)
ed_state = {k.replace("encoder_decoder.", ""): v for k, v in state.items() if k.startswith("encoder_decoder.")}
model.load_state_dict(ed_state, strict=False)

# Invert a Qwen3-Embedding-8B embedding (4096-dim)
device = torch.device("cuda")
model, embedding_transform = model.to(device).eval(), embedding_transform.to(device).eval()

with torch.no_grad():
    emb = torch.tensor(your_embedding, dtype=torch.float32).unsqueeze(0).to(device)
    proj = embedding_transform(emb).reshape(1, 16, hidden)
    out = model.generate(inputs_embeds=proj, attention_mask=torch.ones(1, 16, device=device),
                         max_length=128, num_beams=4, early_stopping=True)
    text = tokenizer.decode(out[0], skip_special_tokens=True)

kennethge123/qwen3-bart-corrector