---
license: mit
language:
- en
---
# RankingGPT-bloom-560m

RankingGPT is a text ranker based on large language models with significant in-domain and out-domain effectiveness.
We provide RankingGPT in different sizes and types, including bloom-560m, bloom-1b1, bloom-3b, bloom-7b, llama2-7b, baichuan2-7b and qwen-7b.

More details please refer to our [paper](https://arxiv.org/abs/2311.16720) and [github](https://github.com/Alibaba-NLP/RankingGPT).


## Usage

Code example
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('RankingGPT-bloom-560m')
model = AutoModelForCausalLM.from_pretrained('RankingGPT-bloom-560m').eval()

query='when should a baby walk'
document='Most babies start to walk around 13 months, but your baby may start walking as early as 9 or 10 months or as late as 15 or 16 months.'

context=f'Document: {document} Query:'
example=context+query

context_enc = tokenizer.encode(context, add_special_tokens=False)
continuation_enc = tokenizer.encode(query, add_special_tokens=False)
model_input = torch.tensor(context_enc+continuation_enc[:-1])
continuation_len = len(continuation_enc)
input_len, = model_input.shape


with torch.no_grad():
    logprobs = torch.nn.functional.log_softmax(model(model_input.unsqueeze(dim=0))[0], dim=-1)[0]

logprobs = logprobs[input_len-continuation_len:]
logprobs = torch.gather(logprobs, 1, torch.tensor(continuation_enc).unsqueeze(-1)).squeeze(-1)
score = torch.sum(logprobs)/logprobs.shape[0]

print(f"Document: {document[:20] + '...'} Score: {score}")
```


### Citation

If you find our paper or models helpful, please consider citing them as follows:

```
@misc{zhang2023rankinggpt,
      title={RankingGPT: Empowering Large Language Models in Text Ranking with Progressive Enhancement}, 
      author={Longhui Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang and Min Zhang},
      year={2023},
      eprint={2311.16720},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
```