Scores on benchmarks

Model rank shown below is with respect to all public models.
X average_language rank X
3 benchmarks
X
0
ceiling
best
median
X behavior_language rank X
1 benchmark
X
0
ceiling
best
median
X Futrell2018-pearsonr v1 [reference] rank X
X
0
ceiling
best
median
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.000 engineering_language rank 11
30 benchmarks
.000
0
ceiling
best
median
.000 SyntaxGym [reference] rank 11
30 benchmarks
.000
0
ceiling
best
median
.000 syntaxgym-npi_src_ever v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_orc_fem v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-number_prep v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_orc_masc v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-number_orc v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npi_orc_any v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npi_orc_ever v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_src_fem v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_prep_masc v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-cleft_modifier v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npz_ambig_mod v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-mvrr_mod v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npz_obj_mod v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-center_embed_mod v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-fgd_pp v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npz_ambig v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_prep_fem v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-fgd_object v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-fgd_subject v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-mvrr v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-fgd_hierarchy v1 [reference] rank 1
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-cleft v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-center_embed v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-subordination_pp-pp v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-subordination_orc-orc v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-subordination v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-reflexive_src_masc v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-subordination_src-src v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-npi_src_any v1 [reference] rank 10
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.000 syntaxgym-number_src v1 [reference] rank 11
.000
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9

How to use

from brainscore_language import load_model
model = load_model("earley-parser-minivocab")
model.start_task(...)
model.start_recording(...)
model.look_at(...)

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}
}
        @inproceedings{gauthier-etal-2020-syntaxgym,
    title = "{S}yntax{G}ym: An Online Platform for Targeted Evaluation of Language Models",
    author = "Gauthier, Jon and Hu, Jennifer and Wilcox, Ethan and Qian, Peng and Levy, Roger",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.10",
    pages = "70--76",
    abstract = "Targeted syntactic evaluations have yielded insights into the generalizations learned by neural network language models. However, this line of research requires an uncommon confluence of skills: both the theoretical knowledge needed to design controlled psycholinguistic experiments, and the technical proficiency needed to train and deploy large-scale language models. We present SyntaxGym, an online platform designed to make targeted evaluations accessible to both experts in NLP and linguistics, reproducible across computing environments, and standardized following the norms of psycholinguistic experimental design. This paper releases two tools of independent value for the computational linguistics community: 1. A website, syntaxgym.org, which centralizes the process of targeted syntactic evaluation and provides easy tools for analysis and visualization; 2. Two command-line tools, {`}syntaxgym{`} and {`}lm-zoo{`}, which allow any user to reproduce targeted syntactic evaluations and general language model inference on their own machine.",
}
        

Layer Commitment

No layer commitments found for this model. Older submissions might not have stored this information but will be updated when evaluated on new benchmarks.

Visual Angle

None degrees