Scores on benchmarks
Model rank shown below is with respect to all public models.| .324 |
average_language
rank 13
6 benchmarks |
|
| .409 |
neural_language
rank 5
5 benchmarks |
|
| .831 |
Pereira2018-ridge
rank 5
2 benchmarks |
|
| .854 |
Pereira2018.243sentences-ridge
v1
rank 6
|
|
|
||
| .808 |
Pereira2018.384sentences-ridge
v1
rank 3
|
|
|
||
| .190 |
Blank2014-ridge
v1
rank 3
|
|
|
||
| .616 |
Fedorenko2016-ridge
v3
rank 9
|
|
|
||
| .239 |
behavior_language
rank 21
1 benchmark |
|
| .239 |
Futrell2018-pearsonr
v1
[reference]
rank 21
|
|
|
||
How to use
from brainscore_language import load_model
model = load_model("qwen3-14b-loc-10")
model.start_task(...)
model.start_recording(...)
model.look_at(...)
Brain Encoding Response Generator (BERG)
Through the BERG you can easily generate neural responses to text sentences of your choice using any Brain-Score language model.
For more information on how to use BERG, see the documentation and tutorial.
Benchmarks bibtex
@proceedings{futrell2018natural,
title={The Natural Stories Corpus},
author={Futrell, Richard and Gibson, Edward and Tily, Harry J. and Blank, Idan and Vishnevetsky, Anastasia and
Piantadosi, Steven T. and Fedorenko, Evelina},
conference={International Conference on Language Resources and Evaluation (LREC)},
url={http://www.lrec-conf.org/proceedings/lrec2018/pdf/337.pdf},
year={2018}
}