|
|
|
--- |
|
|
|
library_name: transformers |
|
license: apache-2.0 |
|
language: |
|
- en |
|
- zh |
|
- es |
|
- de |
|
- ar |
|
- ru |
|
- ja |
|
- ko |
|
- hi |
|
- sk |
|
- vi |
|
- tr |
|
- fi |
|
- id |
|
- fa |
|
- 'no' |
|
- th |
|
- sv |
|
- pt |
|
- da |
|
- bn |
|
- te |
|
- ro |
|
- it |
|
- fr |
|
- nl |
|
- sw |
|
- pl |
|
- hu |
|
- cs |
|
- el |
|
- uk |
|
- mr |
|
- ta |
|
- tl |
|
- bg |
|
- lt |
|
- ur |
|
- he |
|
- gu |
|
- kn |
|
- am |
|
- kk |
|
- hr |
|
- uz |
|
- jv |
|
- ca |
|
- az |
|
- ms |
|
- sr |
|
- sl |
|
- yo |
|
- lv |
|
- is |
|
- ha |
|
- ka |
|
- et |
|
- bs |
|
- hy |
|
- ml |
|
- pa |
|
- mt |
|
- km |
|
- sq |
|
- or |
|
- as |
|
- my |
|
- mn |
|
- af |
|
- be |
|
- ga |
|
- mk |
|
- cy |
|
- gl |
|
- ceb |
|
- la |
|
- yi |
|
- lb |
|
- tg |
|
- gd |
|
- ne |
|
- ps |
|
- eu |
|
- ky |
|
- ku |
|
- si |
|
- ht |
|
- eo |
|
- lo |
|
- fy |
|
- sd |
|
- mg |
|
- so |
|
- ckb |
|
- su |
|
- nn |
|
datasets: |
|
- lightblue/reranker_continuous_filt_max7_train |
|
base_model: |
|
- Qwen/Qwen2.5-0.5B-Instruct |
|
pipeline_tag: text-generation |
|
tags: |
|
- reranker |
|
|
|
--- |
|
|
|
[](https://hf.co/QuantFactory) |
|
|
|
|
|
# QuantFactory/lb-reranker-0.5B-v1.0-GGUF |
|
This is quantized version of [lightblue/lb-reranker-0.5B-v1.0](https://huggingface.co/lightblue/lb-reranker-0.5B-v1.0) created using llama.cpp |
|
|
|
# Original Model Card |
|
|
|
|
|
# LB Reranker v1.0 |
|
|
|
<div style="width: 100%; height: 160px; |
|
display: flex; align-items: center; |
|
justify-content: center; |
|
border: 8px solid black; |
|
font-size: 120px; font-weight: bold; |
|
text-align: center; |
|
color: #438db8; |
|
font-family: 'Helvetica Neue', sans-serif;"> |
|
LBR |
|
</div> |
|
|
|
The LB Reranker has been trained to determine the relatedness of a given query to a piece of text, therefore allowing it to be used as a ranker or reranker in various retrieval-based tasks. |
|
|
|
This model is fine-tuned from a [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model checkpoint and was trained for roughly 5.5 hours using the 8 x L20 instance ([ecs.gn8is-8x.32xlarge](https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families-1)) on [Alibaba Cloud](https://www.alibabacloud.com/). |
|
|
|
The training data for this model can be found at [lightblue/reranker_continuous_filt_max7_train](https://huggingface.co/datasets/lightblue/reranker_continuous_filt_max7_train) and the code for generating this data as well as running the training of the model can be found on [our Github repo](https://github.com/lightblue-tech/lb-reranker). |
|
|
|
Trained on data in over 95 languages, this model is applicable to a broad range of use cases. |
|
|
|
This model has three main benefits over comparable rerankers. |
|
1. It has shown slightly higher performance on evaluation benchmarks. |
|
2. It has been trained on more languages than any previous model. |
|
3. It is a simple Causal LM model trained to output a string between "1" and "7". |
|
|
|
This last point means that this model can be used natively with many widely available inference packages, including vLLM and LMDeploy. |
|
This in turns allows our reranker to benefit from improvements to inference as and when these packages release them. |
|
|
|
Update: We have also found that this model works pretty well as a code snippet reranker too (P@1 of 96%)! See our [Colab](https://colab.research.google.com/drive/1ABL1xaarekLIlVJKbniYhXgYu6ZNwfBm?usp=sharing) for more details. |
|
|
|
# How to use |
|
|
|
The model was trained to expect an input such as: |
|
|
|
``` |
|
<<<Query>>> |
|
{your_query_here} |
|
|
|
<<<Context>>> |
|
{your_context_here} |
|
``` |
|
|
|
And to output a string of a number between 1-7. |
|
|
|
In order to make a continuous score that can be used for reranking query-context pairs (i.e. a method with few ties), we calculate the expectation value of the scores. |
|
|
|
We include scripts to do this in both vLLM and LMDeploy: |
|
|
|
#### vLLM |
|
|
|
Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`. |
|
|
|
```python |
|
from vllm import LLM, SamplingParams |
|
import numpy as np |
|
|
|
def make_reranker_input(t, q): |
|
return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}" |
|
|
|
def make_reranker_training_datum(context, question): |
|
system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related." |
|
|
|
return [ |
|
{"role": "system", "content": system_message}, |
|
{"role": "user", "content": make_reranker_input(context, question)}, |
|
] |
|
|
|
def get_prob(logprob_dict, tok_id): |
|
return np.exp(logprob_dict[tok_id].logprob) if tok_id in logprob_dict.keys() else 0 |
|
|
|
llm = LLM("lightblue/lb-reranker-v1.0") |
|
sampling_params = SamplingParams(temperature=0.0, logprobs=14, max_tokens=1) |
|
tok = llm.llm_engine.tokenizer.tokenizer |
|
idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)] |
|
|
|
query_texts = [ |
|
("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
] |
|
|
|
chats = [make_reranker_training_datum(c, q) for q, c in query_texts] |
|
responses = llm.chat(chats, sampling_params) |
|
probs = np.array([[get_prob(r.outputs[0].logprobs[0], y) for y in idx_tokens] for r in responses]) |
|
|
|
N = probs.shape[1] |
|
M = probs.shape[0] |
|
idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N) |
|
|
|
expected_vals = (probs * idxs).sum(axis=1) |
|
print(expected_vals) |
|
# [6.66570732 1.86686378 1.01102923] |
|
``` |
|
|
|
#### LMDeploy |
|
|
|
Install [LMDeploy](https://github.com/InternLM/lmdeploy) using `pip install lmdeploy`. |
|
|
|
```python |
|
# Un-comment this if running in a Jupyter notebook, Colab etc. |
|
# import nest_asyncio |
|
# nest_asyncio.apply() |
|
|
|
from lmdeploy import GenerationConfig, ChatTemplateConfig, pipeline |
|
import numpy as np |
|
|
|
def make_reranker_input(t, q): |
|
return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}" |
|
|
|
def make_reranker_training_datum(context, question): |
|
system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related." |
|
|
|
return [ |
|
{"role": "system", "content": system_message}, |
|
{"role": "user", "content": make_reranker_input(context, question)}, |
|
] |
|
|
|
def get_prob(logprob_dict, tok_id): |
|
return np.exp(logprob_dict[tok_id]) if tok_id in logprob_dict.keys() else 0 |
|
|
|
pipe = pipeline( |
|
"lightblue/lb-reranker-v1.0", |
|
chat_template_config=ChatTemplateConfig( |
|
model_name='qwen2d5', |
|
capability='chat' |
|
) |
|
) |
|
tok = pipe.tokenizer.model |
|
idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)] |
|
|
|
query_texts = [ |
|
("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."), |
|
] |
|
|
|
chats = [make_reranker_training_datum(c, q) for q, c in query_texts] |
|
responses = pipe( |
|
chats, |
|
gen_config=GenerationConfig(temperature=1.0, logprobs=14, max_new_tokens=1, do_sample=True) |
|
) |
|
probs = np.array([[get_prob(r.logprobs[0], y) for y in idx_tokens] for r in responses]) |
|
|
|
N = probs.shape[1] |
|
M = probs.shape[0] |
|
idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N) |
|
|
|
expected_vals = (probs * idxs).sum(axis=1) |
|
print(expected_vals) |
|
# [6.66415229 1.84342025 1.01133205] |
|
``` |
|
|
|
# Evaluation |
|
|
|
We perform an evaluation on 9 datasets from the [BEIR benchmark](https://github.com/beir-cellar/beir) that none of the evaluated models have been trained upon (to our knowledge). |
|
|
|
* Arguana |
|
* Dbpedia-entity |
|
* Fiqa |
|
* NFcorpus |
|
* Scidocs |
|
* Scifact |
|
* Trec-covid-v2 |
|
* Vihealthqa |
|
* Webis-touche2020 |
|
|
|
We evaluate on a subset of all queries (the first 250) to save evaluation time. |
|
|
|
We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed. |
|
|
|
We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb). |
|
|
|
 |
|
|
|
 |
|
|
|
As we can see, this reranker attains greater IR evaluation metrics compared to the two benchmarks we include for all positions apart from @1. |
|
|
|
 |
|
|
|
We also show that our model is, on average, faster than the BGE reranker v2. |
|
|
|
# License |
|
|
|
We share this model under an Apache 2.0 license. |
|
|
|
# Developed by |
|
|
|
<a href="https://www.lightblue-tech.com"> |
|
<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/> |
|
</a> |
|
|
|
This model was trained by Peter Devine ([ptrdvn](https://huggingface.co/ptrdvn)) for Lightblue |
|
|
