---
license: mit
datasets:
- philipphager/baidu-ultr-pretrain
- philipphager/baidu-ultr_uva-mlm-ctr
metrics:
- dcg@1
- dcg@3
- dcg@5
- dcg@10
- ndcg@10
- mrr@10
---

# Naive Listwise MonoBERT trained on Baidu-ULTR
A flax-based MonoBERT cross encoder trained on the Baidu-ULTR dataset with a **listwise softmax cross-entropy loss on clicks**. The loss is called "naive" as we use user clicks as a signal of relevance without any additional position bias correction. For more info, read our paper here.

## Usage
```Python
import jax.numpy as jnp

from src.model import ListwiseCrossEncoder

model = ListwiseCrossEncoder.from_pretrained(
    "philipphager/baidu-ultr_uva-bert_naive-listwise",
)

# Mock batch following Baidu-ULTR with 4 documents, each with 8 tokens
batch = {
    # Query_id for each document
    "query_id": jnp.array([1, 1, 1, 1]),
    # Document position in SERP
    "positions": jnp.array([1, 2, 3, 4]),
    # Token ids for: [CLS] Query [SEP] Document
    "tokens": jnp.array([
        [2, 21448, 21874, 21436, 1, 20206, 4012, 2860],
        [2, 21448, 21874, 21436, 1, 16794, 4522, 2082],
        [2, 21448, 21874, 21436, 1, 20206, 10082, 9773],
        [2, 21448, 21874, 21436, 1, 2618, 8520, 2860],
    ]),
    # Specify if a token id belongs to the query (0) or document (1)
    "token_types": jnp.array([
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
    ]),
    # Marks if a token should be attended to (True) or ignored, e.g., padding tokens (False):
    "attention_mask": jnp.array([
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
    ]),
}

outputs = model(batch)
print(outputs)
```

## Test Results on Baidu-ULTR Expert Annotations

| Model               | log-likelihood | DCG@1  | DCG@3  | DCG@5  | DCG@10 | nDCG@10 | MRR@10 |
|---------------------|----------------|--------|--------|--------|--------|---------|--------|
| Naive Pointwise     | 0.2272         | 1.6836 | 3.5616 | 4.8822 | 7.4244 | 0.3640  | 0.6096 |
| **Naive Listwise**  | -              | 1.9738 | 4.1609 | 5.6861 | 8.5432 | 0.4091  | 0.6436 |