Commit
•
4e35bff
1
Parent(s):
9e889b2
eval update
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- lm-eval-output/RWKV/v5-Eagle-7B-HF/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +9 -9
- lm-eval-output/RWKV/v5-Eagle-7B-HF/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +132 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +161 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +2249 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +0 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +58 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +374 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +67 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +126 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +252 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +2594 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +66 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +283 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +64 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +0 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +67 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +65 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +58 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +390 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +548 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +423 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +248 -0
- lm-eval-output/SmerkyG/rwkv-5-world-1b5/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +132 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +161 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +2249 -0
- lm-eval-output/SmerkyG/rwkv-5-world-3b/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/RWKV/v5-Eagle-7B-HF/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
CHANGED
@@ -1,30 +1,30 @@
|
|
1 |
{
|
2 |
"results": {
|
3 |
"anli": {
|
4 |
-
"acc,none": 0.
|
5 |
-
"acc_stderr,none": 0.
|
6 |
"alias": "anli"
|
7 |
},
|
8 |
"anli_r1": {
|
9 |
"acc,none": 0.38,
|
10 |
-
"acc_stderr,none": 0.
|
11 |
"alias": " - anli_r1"
|
12 |
},
|
13 |
"anli_r2": {
|
14 |
"acc,none": 0.345,
|
15 |
-
"acc_stderr,none": 0.
|
16 |
"alias": " - anli_r2"
|
17 |
},
|
18 |
"anli_r3": {
|
19 |
-
"acc,none": 0.
|
20 |
-
"acc_stderr,none": 0.
|
21 |
"alias": " - anli_r3"
|
22 |
}
|
23 |
},
|
24 |
"groups": {
|
25 |
"anli": {
|
26 |
-
"acc,none": 0.
|
27 |
-
"acc_stderr,none": 0.
|
28 |
"alias": "anli"
|
29 |
}
|
30 |
},
|
@@ -157,5 +157,5 @@
|
|
157 |
"bootstrap_iters": 100000,
|
158 |
"gen_kwargs": null
|
159 |
},
|
160 |
-
"git_hash": "
|
161 |
}
|
|
|
1 |
{
|
2 |
"results": {
|
3 |
"anli": {
|
4 |
+
"acc,none": 0.3590625,
|
5 |
+
"acc_stderr,none": 0.017704453505961653,
|
6 |
"alias": "anli"
|
7 |
},
|
8 |
"anli_r1": {
|
9 |
"acc,none": 0.38,
|
10 |
+
"acc_stderr,none": 0.015356947477797577,
|
11 |
"alias": " - anli_r1"
|
12 |
},
|
13 |
"anli_r2": {
|
14 |
"acc,none": 0.345,
|
15 |
+
"acc_stderr,none": 0.015039986742055237,
|
16 |
"alias": " - anli_r2"
|
17 |
},
|
18 |
"anli_r3": {
|
19 |
+
"acc,none": 0.35333333333333333,
|
20 |
+
"acc_stderr,none": 0.013804572162314933,
|
21 |
"alias": " - anli_r3"
|
22 |
}
|
23 |
},
|
24 |
"groups": {
|
25 |
"anli": {
|
26 |
+
"acc,none": 0.3590625,
|
27 |
+
"acc_stderr,none": 0.017704453505961653,
|
28 |
"alias": "anli"
|
29 |
}
|
30 |
},
|
|
|
157 |
"bootstrap_iters": 100000,
|
158 |
"gen_kwargs": null
|
159 |
},
|
160 |
+
"git_hash": "1ee41f7"
|
161 |
}
|
lm-eval-output/RWKV/v5-Eagle-7B-HF/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ef7c16a50e1dd8570ebfebb583f105c944453ead8884e1c0d67fe9c41ade6a45
|
3 |
+
size 159064
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"ai2_arc": {
|
4 |
+
"acc,none": 0.512119503945885,
|
5 |
+
"acc_stderr,none": 0.10742116000119395,
|
6 |
+
"acc_norm,none": 0.49408117249154454,
|
7 |
+
"acc_norm_stderr,none": 0.07753732451937403,
|
8 |
+
"alias": "ai2_arc"
|
9 |
+
},
|
10 |
+
"arc_challenge": {
|
11 |
+
"acc,none": 0.28498293515358364,
|
12 |
+
"acc_stderr,none": 0.013191348179838793,
|
13 |
+
"acc_norm,none": 0.3310580204778157,
|
14 |
+
"acc_norm_stderr,none": 0.01375206241981783,
|
15 |
+
"alias": " - arc_challenge"
|
16 |
+
},
|
17 |
+
"arc_easy": {
|
18 |
+
"acc,none": 0.6241582491582491,
|
19 |
+
"acc_stderr,none": 0.009938436373170633,
|
20 |
+
"acc_norm,none": 0.5744949494949495,
|
21 |
+
"acc_norm_stderr,none": 0.010145271182591033,
|
22 |
+
"alias": " - arc_easy"
|
23 |
+
}
|
24 |
+
},
|
25 |
+
"groups": {
|
26 |
+
"ai2_arc": {
|
27 |
+
"acc,none": 0.512119503945885,
|
28 |
+
"acc_stderr,none": 0.10742116000119395,
|
29 |
+
"acc_norm,none": 0.49408117249154454,
|
30 |
+
"acc_norm_stderr,none": 0.07753732451937403,
|
31 |
+
"alias": "ai2_arc"
|
32 |
+
}
|
33 |
+
},
|
34 |
+
"configs": {
|
35 |
+
"arc_challenge": {
|
36 |
+
"task": "arc_challenge",
|
37 |
+
"group": [
|
38 |
+
"ai2_arc"
|
39 |
+
],
|
40 |
+
"dataset_path": "allenai/ai2_arc",
|
41 |
+
"dataset_name": "ARC-Challenge",
|
42 |
+
"training_split": "train",
|
43 |
+
"validation_split": "validation",
|
44 |
+
"test_split": "test",
|
45 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
46 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
47 |
+
"doc_to_choice": "{{choices.text}}",
|
48 |
+
"description": "",
|
49 |
+
"target_delimiter": " ",
|
50 |
+
"fewshot_delimiter": "\n\n",
|
51 |
+
"metric_list": [
|
52 |
+
{
|
53 |
+
"metric": "acc",
|
54 |
+
"aggregation": "mean",
|
55 |
+
"higher_is_better": true
|
56 |
+
},
|
57 |
+
{
|
58 |
+
"metric": "acc_norm",
|
59 |
+
"aggregation": "mean",
|
60 |
+
"higher_is_better": true
|
61 |
+
}
|
62 |
+
],
|
63 |
+
"output_type": "multiple_choice",
|
64 |
+
"repeats": 1,
|
65 |
+
"should_decontaminate": true,
|
66 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
67 |
+
"metadata": {
|
68 |
+
"version": 1.0
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"arc_easy": {
|
72 |
+
"task": "arc_easy",
|
73 |
+
"group": [
|
74 |
+
"ai2_arc"
|
75 |
+
],
|
76 |
+
"dataset_path": "allenai/ai2_arc",
|
77 |
+
"dataset_name": "ARC-Easy",
|
78 |
+
"training_split": "train",
|
79 |
+
"validation_split": "validation",
|
80 |
+
"test_split": "test",
|
81 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
82 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
83 |
+
"doc_to_choice": "{{choices.text}}",
|
84 |
+
"description": "",
|
85 |
+
"target_delimiter": " ",
|
86 |
+
"fewshot_delimiter": "\n\n",
|
87 |
+
"metric_list": [
|
88 |
+
{
|
89 |
+
"metric": "acc",
|
90 |
+
"aggregation": "mean",
|
91 |
+
"higher_is_better": true
|
92 |
+
},
|
93 |
+
{
|
94 |
+
"metric": "acc_norm",
|
95 |
+
"aggregation": "mean",
|
96 |
+
"higher_is_better": true
|
97 |
+
}
|
98 |
+
],
|
99 |
+
"output_type": "multiple_choice",
|
100 |
+
"repeats": 1,
|
101 |
+
"should_decontaminate": true,
|
102 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
103 |
+
"metadata": {
|
104 |
+
"version": 1.0
|
105 |
+
}
|
106 |
+
}
|
107 |
+
},
|
108 |
+
"versions": {
|
109 |
+
"ai2_arc": "N/A",
|
110 |
+
"arc_challenge": 1.0,
|
111 |
+
"arc_easy": 1.0
|
112 |
+
},
|
113 |
+
"n-shot": {
|
114 |
+
"ai2_arc": 0,
|
115 |
+
"arc_challenge": 0,
|
116 |
+
"arc_easy": 0
|
117 |
+
},
|
118 |
+
"config": {
|
119 |
+
"model": "hf",
|
120 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
121 |
+
"batch_size": "auto",
|
122 |
+
"batch_sizes": [
|
123 |
+
64
|
124 |
+
],
|
125 |
+
"device": null,
|
126 |
+
"use_cache": null,
|
127 |
+
"limit": null,
|
128 |
+
"bootstrap_iters": 100000,
|
129 |
+
"gen_kwargs": null
|
130 |
+
},
|
131 |
+
"git_hash": "1ee41f7"
|
132 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:70498c2bbc6277b14857387a1cb10f42fdaa43ffad760b6a120585e3cc73d959
|
3 |
+
size 48938
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,161 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"anli": {
|
4 |
+
"acc,none": 0.3446875,
|
5 |
+
"acc_stderr,none": 0.016201421596492432,
|
6 |
+
"alias": "anli"
|
7 |
+
},
|
8 |
+
"anli_r1": {
|
9 |
+
"acc,none": 0.358,
|
10 |
+
"acc_stderr,none": 0.01516792886540756,
|
11 |
+
"alias": " - anli_r1"
|
12 |
+
},
|
13 |
+
"anli_r2": {
|
14 |
+
"acc,none": 0.33,
|
15 |
+
"acc_stderr,none": 0.014876872027456727,
|
16 |
+
"alias": " - anli_r2"
|
17 |
+
},
|
18 |
+
"anli_r3": {
|
19 |
+
"acc,none": 0.3458333333333333,
|
20 |
+
"acc_stderr,none": 0.013736245342311012,
|
21 |
+
"alias": " - anli_r3"
|
22 |
+
}
|
23 |
+
},
|
24 |
+
"groups": {
|
25 |
+
"anli": {
|
26 |
+
"acc,none": 0.3446875,
|
27 |
+
"acc_stderr,none": 0.016201421596492432,
|
28 |
+
"alias": "anli"
|
29 |
+
}
|
30 |
+
},
|
31 |
+
"configs": {
|
32 |
+
"anli_r1": {
|
33 |
+
"task": "anli_r1",
|
34 |
+
"group": [
|
35 |
+
"anli"
|
36 |
+
],
|
37 |
+
"dataset_path": "anli",
|
38 |
+
"training_split": "train_r1",
|
39 |
+
"validation_split": "dev_r1",
|
40 |
+
"test_split": "test_r1",
|
41 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
42 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
43 |
+
"doc_to_choice": [
|
44 |
+
"True",
|
45 |
+
"Neither",
|
46 |
+
"False"
|
47 |
+
],
|
48 |
+
"description": "",
|
49 |
+
"target_delimiter": " ",
|
50 |
+
"fewshot_delimiter": "\n\n",
|
51 |
+
"metric_list": [
|
52 |
+
{
|
53 |
+
"metric": "acc",
|
54 |
+
"aggregation": "mean",
|
55 |
+
"higher_is_better": true
|
56 |
+
}
|
57 |
+
],
|
58 |
+
"output_type": "multiple_choice",
|
59 |
+
"repeats": 1,
|
60 |
+
"should_decontaminate": true,
|
61 |
+
"doc_to_decontamination_query": "premise",
|
62 |
+
"metadata": {
|
63 |
+
"version": 1.0
|
64 |
+
}
|
65 |
+
},
|
66 |
+
"anli_r2": {
|
67 |
+
"task": "anli_r2",
|
68 |
+
"group": [
|
69 |
+
"anli"
|
70 |
+
],
|
71 |
+
"dataset_path": "anli",
|
72 |
+
"training_split": "train_r2",
|
73 |
+
"validation_split": "dev_r2",
|
74 |
+
"test_split": "test_r2",
|
75 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
76 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
77 |
+
"doc_to_choice": [
|
78 |
+
"True",
|
79 |
+
"Neither",
|
80 |
+
"False"
|
81 |
+
],
|
82 |
+
"description": "",
|
83 |
+
"target_delimiter": " ",
|
84 |
+
"fewshot_delimiter": "\n\n",
|
85 |
+
"metric_list": [
|
86 |
+
{
|
87 |
+
"metric": "acc",
|
88 |
+
"aggregation": "mean",
|
89 |
+
"higher_is_better": true
|
90 |
+
}
|
91 |
+
],
|
92 |
+
"output_type": "multiple_choice",
|
93 |
+
"repeats": 1,
|
94 |
+
"should_decontaminate": true,
|
95 |
+
"doc_to_decontamination_query": "premise",
|
96 |
+
"metadata": {
|
97 |
+
"version": 1.0
|
98 |
+
}
|
99 |
+
},
|
100 |
+
"anli_r3": {
|
101 |
+
"task": "anli_r3",
|
102 |
+
"group": [
|
103 |
+
"anli"
|
104 |
+
],
|
105 |
+
"dataset_path": "anli",
|
106 |
+
"training_split": "train_r3",
|
107 |
+
"validation_split": "dev_r3",
|
108 |
+
"test_split": "test_r3",
|
109 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
110 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
111 |
+
"doc_to_choice": [
|
112 |
+
"True",
|
113 |
+
"Neither",
|
114 |
+
"False"
|
115 |
+
],
|
116 |
+
"description": "",
|
117 |
+
"target_delimiter": " ",
|
118 |
+
"fewshot_delimiter": "\n\n",
|
119 |
+
"metric_list": [
|
120 |
+
{
|
121 |
+
"metric": "acc",
|
122 |
+
"aggregation": "mean",
|
123 |
+
"higher_is_better": true
|
124 |
+
}
|
125 |
+
],
|
126 |
+
"output_type": "multiple_choice",
|
127 |
+
"repeats": 1,
|
128 |
+
"should_decontaminate": true,
|
129 |
+
"doc_to_decontamination_query": "premise",
|
130 |
+
"metadata": {
|
131 |
+
"version": 1.0
|
132 |
+
}
|
133 |
+
}
|
134 |
+
},
|
135 |
+
"versions": {
|
136 |
+
"anli": "N/A",
|
137 |
+
"anli_r1": 1.0,
|
138 |
+
"anli_r2": 1.0,
|
139 |
+
"anli_r3": 1.0
|
140 |
+
},
|
141 |
+
"n-shot": {
|
142 |
+
"anli": 0,
|
143 |
+
"anli_r1": 0,
|
144 |
+
"anli_r2": 0,
|
145 |
+
"anli_r3": 0
|
146 |
+
},
|
147 |
+
"config": {
|
148 |
+
"model": "hf",
|
149 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
150 |
+
"batch_size": "auto",
|
151 |
+
"batch_sizes": [
|
152 |
+
64
|
153 |
+
],
|
154 |
+
"device": null,
|
155 |
+
"use_cache": null,
|
156 |
+
"limit": null,
|
157 |
+
"bootstrap_iters": 100000,
|
158 |
+
"gen_kwargs": null
|
159 |
+
},
|
160 |
+
"git_hash": "1ee41f7"
|
161 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e81f489289541497f6c037de418a934e664fce533485d8aa44fdd232df89245e
|
3 |
+
size 42769
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,2249 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"blimp": {
|
4 |
+
"acc,none": 0.8336119402985075,
|
5 |
+
"acc_stderr,none": 0.1509763959549486,
|
6 |
+
"alias": "blimp"
|
7 |
+
},
|
8 |
+
"blimp_adjunct_island": {
|
9 |
+
"acc,none": 0.9,
|
10 |
+
"acc_stderr,none": 0.00949157995752507,
|
11 |
+
"alias": " - blimp_adjunct_island"
|
12 |
+
},
|
13 |
+
"blimp_anaphor_gender_agreement": {
|
14 |
+
"acc,none": 0.992,
|
15 |
+
"acc_stderr,none": 0.0028185003005045057,
|
16 |
+
"alias": " - blimp_anaphor_gender_agreement"
|
17 |
+
},
|
18 |
+
"blimp_anaphor_number_agreement": {
|
19 |
+
"acc,none": 0.995,
|
20 |
+
"acc_stderr,none": 0.00223158687484488,
|
21 |
+
"alias": " - blimp_anaphor_number_agreement"
|
22 |
+
},
|
23 |
+
"blimp_animate_subject_passive": {
|
24 |
+
"acc,none": 0.797,
|
25 |
+
"acc_stderr,none": 0.012726073744598275,
|
26 |
+
"alias": " - blimp_animate_subject_passive"
|
27 |
+
},
|
28 |
+
"blimp_animate_subject_trans": {
|
29 |
+
"acc,none": 0.907,
|
30 |
+
"acc_stderr,none": 0.009188875634996693,
|
31 |
+
"alias": " - blimp_animate_subject_trans"
|
32 |
+
},
|
33 |
+
"blimp_causative": {
|
34 |
+
"acc,none": 0.779,
|
35 |
+
"acc_stderr,none": 0.013127502859696244,
|
36 |
+
"alias": " - blimp_causative"
|
37 |
+
},
|
38 |
+
"blimp_complex_NP_island": {
|
39 |
+
"acc,none": 0.654,
|
40 |
+
"acc_stderr,none": 0.015050266127564441,
|
41 |
+
"alias": " - blimp_complex_NP_island"
|
42 |
+
},
|
43 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
44 |
+
"acc,none": 0.742,
|
45 |
+
"acc_stderr,none": 0.013842963108656603,
|
46 |
+
"alias": " - blimp_coordinate_structure_constraint_complex_left_branch"
|
47 |
+
},
|
48 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
49 |
+
"acc,none": 0.85,
|
50 |
+
"acc_stderr,none": 0.0112972398234093,
|
51 |
+
"alias": " - blimp_coordinate_structure_constraint_object_extraction"
|
52 |
+
},
|
53 |
+
"blimp_determiner_noun_agreement_1": {
|
54 |
+
"acc,none": 0.998,
|
55 |
+
"acc_stderr,none": 0.001413505570557816,
|
56 |
+
"alias": " - blimp_determiner_noun_agreement_1"
|
57 |
+
},
|
58 |
+
"blimp_determiner_noun_agreement_2": {
|
59 |
+
"acc,none": 0.991,
|
60 |
+
"acc_stderr,none": 0.002987963843142644,
|
61 |
+
"alias": " - blimp_determiner_noun_agreement_2"
|
62 |
+
},
|
63 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
64 |
+
"acc,none": 0.963,
|
65 |
+
"acc_stderr,none": 0.005972157622389635,
|
66 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_1"
|
67 |
+
},
|
68 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
69 |
+
"acc,none": 0.955,
|
70 |
+
"acc_stderr,none": 0.0065588122414061405,
|
71 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_2"
|
72 |
+
},
|
73 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
74 |
+
"acc,none": 0.961,
|
75 |
+
"acc_stderr,none": 0.006125072776426103,
|
76 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_2"
|
77 |
+
},
|
78 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
79 |
+
"acc,none": 0.929,
|
80 |
+
"acc_stderr,none": 0.008125578442487924,
|
81 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_1"
|
82 |
+
},
|
83 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
84 |
+
"acc,none": 0.924,
|
85 |
+
"acc_stderr,none": 0.008384169266796398,
|
86 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_2"
|
87 |
+
},
|
88 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
89 |
+
"acc,none": 0.982,
|
90 |
+
"acc_stderr,none": 0.004206387249611461,
|
91 |
+
"alias": " - blimp_determiner_noun_agreement_with_adjective_1"
|
92 |
+
},
|
93 |
+
"blimp_distractor_agreement_relational_noun": {
|
94 |
+
"acc,none": 0.881,
|
95 |
+
"acc_stderr,none": 0.010244215145336667,
|
96 |
+
"alias": " - blimp_distractor_agreement_relational_noun"
|
97 |
+
},
|
98 |
+
"blimp_distractor_agreement_relative_clause": {
|
99 |
+
"acc,none": 0.797,
|
100 |
+
"acc_stderr,none": 0.01272607374459827,
|
101 |
+
"alias": " - blimp_distractor_agreement_relative_clause"
|
102 |
+
},
|
103 |
+
"blimp_drop_argument": {
|
104 |
+
"acc,none": 0.806,
|
105 |
+
"acc_stderr,none": 0.012510816141264366,
|
106 |
+
"alias": " - blimp_drop_argument"
|
107 |
+
},
|
108 |
+
"blimp_ellipsis_n_bar_1": {
|
109 |
+
"acc,none": 0.852,
|
110 |
+
"acc_stderr,none": 0.011234866364235261,
|
111 |
+
"alias": " - blimp_ellipsis_n_bar_1"
|
112 |
+
},
|
113 |
+
"blimp_ellipsis_n_bar_2": {
|
114 |
+
"acc,none": 0.883,
|
115 |
+
"acc_stderr,none": 0.010169287802713327,
|
116 |
+
"alias": " - blimp_ellipsis_n_bar_2"
|
117 |
+
},
|
118 |
+
"blimp_existential_there_object_raising": {
|
119 |
+
"acc,none": 0.843,
|
120 |
+
"acc_stderr,none": 0.011510146979230177,
|
121 |
+
"alias": " - blimp_existential_there_object_raising"
|
122 |
+
},
|
123 |
+
"blimp_existential_there_quantifiers_1": {
|
124 |
+
"acc,none": 0.989,
|
125 |
+
"acc_stderr,none": 0.0032999833166078166,
|
126 |
+
"alias": " - blimp_existential_there_quantifiers_1"
|
127 |
+
},
|
128 |
+
"blimp_existential_there_quantifiers_2": {
|
129 |
+
"acc,none": 0.27,
|
130 |
+
"acc_stderr,none": 0.014046255632633915,
|
131 |
+
"alias": " - blimp_existential_there_quantifiers_2"
|
132 |
+
},
|
133 |
+
"blimp_existential_there_subject_raising": {
|
134 |
+
"acc,none": 0.928,
|
135 |
+
"acc_stderr,none": 0.008178195576218681,
|
136 |
+
"alias": " - blimp_existential_there_subject_raising"
|
137 |
+
},
|
138 |
+
"blimp_expletive_it_object_raising": {
|
139 |
+
"acc,none": 0.827,
|
140 |
+
"acc_stderr,none": 0.011967214137559927,
|
141 |
+
"alias": " - blimp_expletive_it_object_raising"
|
142 |
+
},
|
143 |
+
"blimp_inchoative": {
|
144 |
+
"acc,none": 0.696,
|
145 |
+
"acc_stderr,none": 0.014553205687950436,
|
146 |
+
"alias": " - blimp_inchoative"
|
147 |
+
},
|
148 |
+
"blimp_intransitive": {
|
149 |
+
"acc,none": 0.856,
|
150 |
+
"acc_stderr,none": 0.01110798754893915,
|
151 |
+
"alias": " - blimp_intransitive"
|
152 |
+
},
|
153 |
+
"blimp_irregular_past_participle_adjectives": {
|
154 |
+
"acc,none": 0.994,
|
155 |
+
"acc_stderr,none": 0.002443352199329801,
|
156 |
+
"alias": " - blimp_irregular_past_participle_adjectives"
|
157 |
+
},
|
158 |
+
"blimp_irregular_past_participle_verbs": {
|
159 |
+
"acc,none": 0.915,
|
160 |
+
"acc_stderr,none": 0.008823426366942305,
|
161 |
+
"alias": " - blimp_irregular_past_participle_verbs"
|
162 |
+
},
|
163 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
164 |
+
"acc,none": 0.937,
|
165 |
+
"acc_stderr,none": 0.007687007876286419,
|
166 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_1"
|
167 |
+
},
|
168 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
169 |
+
"acc,none": 0.927,
|
170 |
+
"acc_stderr,none": 0.00823035471524406,
|
171 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_2"
|
172 |
+
},
|
173 |
+
"blimp_left_branch_island_echo_question": {
|
174 |
+
"acc,none": 0.45,
|
175 |
+
"acc_stderr,none": 0.015740004693383852,
|
176 |
+
"alias": " - blimp_left_branch_island_echo_question"
|
177 |
+
},
|
178 |
+
"blimp_left_branch_island_simple_question": {
|
179 |
+
"acc,none": 0.851,
|
180 |
+
"acc_stderr,none": 0.011266140684632156,
|
181 |
+
"alias": " - blimp_left_branch_island_simple_question"
|
182 |
+
},
|
183 |
+
"blimp_matrix_question_npi_licensor_present": {
|
184 |
+
"acc,none": 0.708,
|
185 |
+
"acc_stderr,none": 0.014385511563477343,
|
186 |
+
"alias": " - blimp_matrix_question_npi_licensor_present"
|
187 |
+
},
|
188 |
+
"blimp_npi_present_1": {
|
189 |
+
"acc,none": 0.577,
|
190 |
+
"acc_stderr,none": 0.015630589090476345,
|
191 |
+
"alias": " - blimp_npi_present_1"
|
192 |
+
},
|
193 |
+
"blimp_npi_present_2": {
|
194 |
+
"acc,none": 0.668,
|
195 |
+
"acc_stderr,none": 0.01489959724281148,
|
196 |
+
"alias": " - blimp_npi_present_2"
|
197 |
+
},
|
198 |
+
"blimp_only_npi_licensor_present": {
|
199 |
+
"acc,none": 0.971,
|
200 |
+
"acc_stderr,none": 0.005309160685757018,
|
201 |
+
"alias": " - blimp_only_npi_licensor_present"
|
202 |
+
},
|
203 |
+
"blimp_only_npi_scope": {
|
204 |
+
"acc,none": 0.733,
|
205 |
+
"acc_stderr,none": 0.013996674851796273,
|
206 |
+
"alias": " - blimp_only_npi_scope"
|
207 |
+
},
|
208 |
+
"blimp_passive_1": {
|
209 |
+
"acc,none": 0.907,
|
210 |
+
"acc_stderr,none": 0.009188875634996697,
|
211 |
+
"alias": " - blimp_passive_1"
|
212 |
+
},
|
213 |
+
"blimp_passive_2": {
|
214 |
+
"acc,none": 0.908,
|
215 |
+
"acc_stderr,none": 0.0091443763931511,
|
216 |
+
"alias": " - blimp_passive_2"
|
217 |
+
},
|
218 |
+
"blimp_principle_A_c_command": {
|
219 |
+
"acc,none": 0.839,
|
220 |
+
"acc_stderr,none": 0.011628164696727193,
|
221 |
+
"alias": " - blimp_principle_A_c_command"
|
222 |
+
},
|
223 |
+
"blimp_principle_A_case_1": {
|
224 |
+
"acc,none": 1.0,
|
225 |
+
"acc_stderr,none": 0.0,
|
226 |
+
"alias": " - blimp_principle_A_case_1"
|
227 |
+
},
|
228 |
+
"blimp_principle_A_case_2": {
|
229 |
+
"acc,none": 0.965,
|
230 |
+
"acc_stderr,none": 0.005814534272734976,
|
231 |
+
"alias": " - blimp_principle_A_case_2"
|
232 |
+
},
|
233 |
+
"blimp_principle_A_domain_1": {
|
234 |
+
"acc,none": 0.994,
|
235 |
+
"acc_stderr,none": 0.0024433521993298415,
|
236 |
+
"alias": " - blimp_principle_A_domain_1"
|
237 |
+
},
|
238 |
+
"blimp_principle_A_domain_2": {
|
239 |
+
"acc,none": 0.9,
|
240 |
+
"acc_stderr,none": 0.009491579957525054,
|
241 |
+
"alias": " - blimp_principle_A_domain_2"
|
242 |
+
},
|
243 |
+
"blimp_principle_A_domain_3": {
|
244 |
+
"acc,none": 0.756,
|
245 |
+
"acc_stderr,none": 0.013588548437881418,
|
246 |
+
"alias": " - blimp_principle_A_domain_3"
|
247 |
+
},
|
248 |
+
"blimp_principle_A_reconstruction": {
|
249 |
+
"acc,none": 0.47,
|
250 |
+
"acc_stderr,none": 0.015790799515836763,
|
251 |
+
"alias": " - blimp_principle_A_reconstruction"
|
252 |
+
},
|
253 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
254 |
+
"acc,none": 0.965,
|
255 |
+
"acc_stderr,none": 0.005814534272734965,
|
256 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_1"
|
257 |
+
},
|
258 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
259 |
+
"acc,none": 0.909,
|
260 |
+
"acc_stderr,none": 0.009099549538400248,
|
261 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_2"
|
262 |
+
},
|
263 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
264 |
+
"acc,none": 0.985,
|
265 |
+
"acc_stderr,none": 0.003845749574503012,
|
266 |
+
"alias": " - blimp_sentential_negation_npi_licensor_present"
|
267 |
+
},
|
268 |
+
"blimp_sentential_negation_npi_scope": {
|
269 |
+
"acc,none": 0.759,
|
270 |
+
"acc_stderr,none": 0.01353152253451541,
|
271 |
+
"alias": " - blimp_sentential_negation_npi_scope"
|
272 |
+
},
|
273 |
+
"blimp_sentential_subject_island": {
|
274 |
+
"acc,none": 0.455,
|
275 |
+
"acc_stderr,none": 0.01575510149834709,
|
276 |
+
"alias": " - blimp_sentential_subject_island"
|
277 |
+
},
|
278 |
+
"blimp_superlative_quantifiers_1": {
|
279 |
+
"acc,none": 0.848,
|
280 |
+
"acc_stderr,none": 0.01135891830347528,
|
281 |
+
"alias": " - blimp_superlative_quantifiers_1"
|
282 |
+
},
|
283 |
+
"blimp_superlative_quantifiers_2": {
|
284 |
+
"acc,none": 0.75,
|
285 |
+
"acc_stderr,none": 0.013699915608779773,
|
286 |
+
"alias": " - blimp_superlative_quantifiers_2"
|
287 |
+
},
|
288 |
+
"blimp_tough_vs_raising_1": {
|
289 |
+
"acc,none": 0.709,
|
290 |
+
"acc_stderr,none": 0.014370995982377953,
|
291 |
+
"alias": " - blimp_tough_vs_raising_1"
|
292 |
+
},
|
293 |
+
"blimp_tough_vs_raising_2": {
|
294 |
+
"acc,none": 0.877,
|
295 |
+
"acc_stderr,none": 0.010391293421849883,
|
296 |
+
"alias": " - blimp_tough_vs_raising_2"
|
297 |
+
},
|
298 |
+
"blimp_transitive": {
|
299 |
+
"acc,none": 0.891,
|
300 |
+
"acc_stderr,none": 0.009859828407037195,
|
301 |
+
"alias": " - blimp_transitive"
|
302 |
+
},
|
303 |
+
"blimp_wh_island": {
|
304 |
+
"acc,none": 0.762,
|
305 |
+
"acc_stderr,none": 0.01347358666196722,
|
306 |
+
"alias": " - blimp_wh_island"
|
307 |
+
},
|
308 |
+
"blimp_wh_questions_object_gap": {
|
309 |
+
"acc,none": 0.865,
|
310 |
+
"acc_stderr,none": 0.010811655372416053,
|
311 |
+
"alias": " - blimp_wh_questions_object_gap"
|
312 |
+
},
|
313 |
+
"blimp_wh_questions_subject_gap": {
|
314 |
+
"acc,none": 0.949,
|
315 |
+
"acc_stderr,none": 0.006960420062571401,
|
316 |
+
"alias": " - blimp_wh_questions_subject_gap"
|
317 |
+
},
|
318 |
+
"blimp_wh_questions_subject_gap_long_distance": {
|
319 |
+
"acc,none": 0.909,
|
320 |
+
"acc_stderr,none": 0.00909954953840024,
|
321 |
+
"alias": " - blimp_wh_questions_subject_gap_long_distance"
|
322 |
+
},
|
323 |
+
"blimp_wh_vs_that_no_gap": {
|
324 |
+
"acc,none": 0.975,
|
325 |
+
"acc_stderr,none": 0.004939574819698455,
|
326 |
+
"alias": " - blimp_wh_vs_that_no_gap"
|
327 |
+
},
|
328 |
+
"blimp_wh_vs_that_no_gap_long_distance": {
|
329 |
+
"acc,none": 0.962,
|
330 |
+
"acc_stderr,none": 0.006049181150584934,
|
331 |
+
"alias": " - blimp_wh_vs_that_no_gap_long_distance"
|
332 |
+
},
|
333 |
+
"blimp_wh_vs_that_with_gap": {
|
334 |
+
"acc,none": 0.467,
|
335 |
+
"acc_stderr,none": 0.015784807891138786,
|
336 |
+
"alias": " - blimp_wh_vs_that_with_gap"
|
337 |
+
},
|
338 |
+
"blimp_wh_vs_that_with_gap_long_distance": {
|
339 |
+
"acc,none": 0.398,
|
340 |
+
"acc_stderr,none": 0.015486634102858924,
|
341 |
+
"alias": " - blimp_wh_vs_that_with_gap_long_distance"
|
342 |
+
}
|
343 |
+
},
|
344 |
+
"groups": {
|
345 |
+
"blimp": {
|
346 |
+
"acc,none": 0.8336119402985075,
|
347 |
+
"acc_stderr,none": 0.1509763959549486,
|
348 |
+
"alias": "blimp"
|
349 |
+
}
|
350 |
+
},
|
351 |
+
"configs": {
|
352 |
+
"blimp_adjunct_island": {
|
353 |
+
"task": "blimp_adjunct_island",
|
354 |
+
"group": "blimp",
|
355 |
+
"dataset_path": "blimp",
|
356 |
+
"dataset_name": "adjunct_island",
|
357 |
+
"validation_split": "train",
|
358 |
+
"doc_to_text": "",
|
359 |
+
"doc_to_target": 0,
|
360 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
361 |
+
"description": "",
|
362 |
+
"target_delimiter": " ",
|
363 |
+
"fewshot_delimiter": "\n\n",
|
364 |
+
"num_fewshot": 0,
|
365 |
+
"metric_list": [
|
366 |
+
{
|
367 |
+
"metric": "acc"
|
368 |
+
}
|
369 |
+
],
|
370 |
+
"output_type": "multiple_choice",
|
371 |
+
"repeats": 1,
|
372 |
+
"should_decontaminate": true,
|
373 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
374 |
+
"metadata": {
|
375 |
+
"version": 1.0
|
376 |
+
}
|
377 |
+
},
|
378 |
+
"blimp_anaphor_gender_agreement": {
|
379 |
+
"task": "blimp_anaphor_gender_agreement",
|
380 |
+
"group": "blimp",
|
381 |
+
"dataset_path": "blimp",
|
382 |
+
"dataset_name": "anaphor_gender_agreement",
|
383 |
+
"validation_split": "train",
|
384 |
+
"doc_to_text": "",
|
385 |
+
"doc_to_target": 0,
|
386 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
387 |
+
"description": "",
|
388 |
+
"target_delimiter": " ",
|
389 |
+
"fewshot_delimiter": "\n\n",
|
390 |
+
"num_fewshot": 0,
|
391 |
+
"metric_list": [
|
392 |
+
{
|
393 |
+
"metric": "acc"
|
394 |
+
}
|
395 |
+
],
|
396 |
+
"output_type": "multiple_choice",
|
397 |
+
"repeats": 1,
|
398 |
+
"should_decontaminate": true,
|
399 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
400 |
+
"metadata": {
|
401 |
+
"version": 1.0
|
402 |
+
}
|
403 |
+
},
|
404 |
+
"blimp_anaphor_number_agreement": {
|
405 |
+
"task": "blimp_anaphor_number_agreement",
|
406 |
+
"group": "blimp",
|
407 |
+
"dataset_path": "blimp",
|
408 |
+
"dataset_name": "anaphor_number_agreement",
|
409 |
+
"validation_split": "train",
|
410 |
+
"doc_to_text": "",
|
411 |
+
"doc_to_target": 0,
|
412 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
413 |
+
"description": "",
|
414 |
+
"target_delimiter": " ",
|
415 |
+
"fewshot_delimiter": "\n\n",
|
416 |
+
"num_fewshot": 0,
|
417 |
+
"metric_list": [
|
418 |
+
{
|
419 |
+
"metric": "acc"
|
420 |
+
}
|
421 |
+
],
|
422 |
+
"output_type": "multiple_choice",
|
423 |
+
"repeats": 1,
|
424 |
+
"should_decontaminate": true,
|
425 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
426 |
+
"metadata": {
|
427 |
+
"version": 1.0
|
428 |
+
}
|
429 |
+
},
|
430 |
+
"blimp_animate_subject_passive": {
|
431 |
+
"task": "blimp_animate_subject_passive",
|
432 |
+
"group": "blimp",
|
433 |
+
"dataset_path": "blimp",
|
434 |
+
"dataset_name": "animate_subject_passive",
|
435 |
+
"validation_split": "train",
|
436 |
+
"doc_to_text": "",
|
437 |
+
"doc_to_target": 0,
|
438 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
439 |
+
"description": "",
|
440 |
+
"target_delimiter": " ",
|
441 |
+
"fewshot_delimiter": "\n\n",
|
442 |
+
"num_fewshot": 0,
|
443 |
+
"metric_list": [
|
444 |
+
{
|
445 |
+
"metric": "acc"
|
446 |
+
}
|
447 |
+
],
|
448 |
+
"output_type": "multiple_choice",
|
449 |
+
"repeats": 1,
|
450 |
+
"should_decontaminate": true,
|
451 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
452 |
+
"metadata": {
|
453 |
+
"version": 1.0
|
454 |
+
}
|
455 |
+
},
|
456 |
+
"blimp_animate_subject_trans": {
|
457 |
+
"task": "blimp_animate_subject_trans",
|
458 |
+
"group": "blimp",
|
459 |
+
"dataset_path": "blimp",
|
460 |
+
"dataset_name": "animate_subject_trans",
|
461 |
+
"validation_split": "train",
|
462 |
+
"doc_to_text": "",
|
463 |
+
"doc_to_target": 0,
|
464 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
465 |
+
"description": "",
|
466 |
+
"target_delimiter": " ",
|
467 |
+
"fewshot_delimiter": "\n\n",
|
468 |
+
"num_fewshot": 0,
|
469 |
+
"metric_list": [
|
470 |
+
{
|
471 |
+
"metric": "acc"
|
472 |
+
}
|
473 |
+
],
|
474 |
+
"output_type": "multiple_choice",
|
475 |
+
"repeats": 1,
|
476 |
+
"should_decontaminate": true,
|
477 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
478 |
+
"metadata": {
|
479 |
+
"version": 1.0
|
480 |
+
}
|
481 |
+
},
|
482 |
+
"blimp_causative": {
|
483 |
+
"task": "blimp_causative",
|
484 |
+
"group": "blimp",
|
485 |
+
"dataset_path": "blimp",
|
486 |
+
"dataset_name": "causative",
|
487 |
+
"validation_split": "train",
|
488 |
+
"doc_to_text": "",
|
489 |
+
"doc_to_target": 0,
|
490 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
491 |
+
"description": "",
|
492 |
+
"target_delimiter": " ",
|
493 |
+
"fewshot_delimiter": "\n\n",
|
494 |
+
"num_fewshot": 0,
|
495 |
+
"metric_list": [
|
496 |
+
{
|
497 |
+
"metric": "acc"
|
498 |
+
}
|
499 |
+
],
|
500 |
+
"output_type": "multiple_choice",
|
501 |
+
"repeats": 1,
|
502 |
+
"should_decontaminate": true,
|
503 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
504 |
+
"metadata": {
|
505 |
+
"version": 1.0
|
506 |
+
}
|
507 |
+
},
|
508 |
+
"blimp_complex_NP_island": {
|
509 |
+
"task": "blimp_complex_NP_island",
|
510 |
+
"group": "blimp",
|
511 |
+
"dataset_path": "blimp",
|
512 |
+
"dataset_name": "complex_NP_island",
|
513 |
+
"validation_split": "train",
|
514 |
+
"doc_to_text": "",
|
515 |
+
"doc_to_target": 0,
|
516 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
517 |
+
"description": "",
|
518 |
+
"target_delimiter": " ",
|
519 |
+
"fewshot_delimiter": "\n\n",
|
520 |
+
"num_fewshot": 0,
|
521 |
+
"metric_list": [
|
522 |
+
{
|
523 |
+
"metric": "acc"
|
524 |
+
}
|
525 |
+
],
|
526 |
+
"output_type": "multiple_choice",
|
527 |
+
"repeats": 1,
|
528 |
+
"should_decontaminate": true,
|
529 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
530 |
+
"metadata": {
|
531 |
+
"version": 1.0
|
532 |
+
}
|
533 |
+
},
|
534 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
535 |
+
"task": "blimp_coordinate_structure_constraint_complex_left_branch",
|
536 |
+
"group": "blimp",
|
537 |
+
"dataset_path": "blimp",
|
538 |
+
"dataset_name": "coordinate_structure_constraint_complex_left_branch",
|
539 |
+
"validation_split": "train",
|
540 |
+
"doc_to_text": "",
|
541 |
+
"doc_to_target": 0,
|
542 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
543 |
+
"description": "",
|
544 |
+
"target_delimiter": " ",
|
545 |
+
"fewshot_delimiter": "\n\n",
|
546 |
+
"num_fewshot": 0,
|
547 |
+
"metric_list": [
|
548 |
+
{
|
549 |
+
"metric": "acc"
|
550 |
+
}
|
551 |
+
],
|
552 |
+
"output_type": "multiple_choice",
|
553 |
+
"repeats": 1,
|
554 |
+
"should_decontaminate": true,
|
555 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
556 |
+
"metadata": {
|
557 |
+
"version": 1.0
|
558 |
+
}
|
559 |
+
},
|
560 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
561 |
+
"task": "blimp_coordinate_structure_constraint_object_extraction",
|
562 |
+
"group": "blimp",
|
563 |
+
"dataset_path": "blimp",
|
564 |
+
"dataset_name": "coordinate_structure_constraint_object_extraction",
|
565 |
+
"validation_split": "train",
|
566 |
+
"doc_to_text": "",
|
567 |
+
"doc_to_target": 0,
|
568 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
569 |
+
"description": "",
|
570 |
+
"target_delimiter": " ",
|
571 |
+
"fewshot_delimiter": "\n\n",
|
572 |
+
"num_fewshot": 0,
|
573 |
+
"metric_list": [
|
574 |
+
{
|
575 |
+
"metric": "acc"
|
576 |
+
}
|
577 |
+
],
|
578 |
+
"output_type": "multiple_choice",
|
579 |
+
"repeats": 1,
|
580 |
+
"should_decontaminate": true,
|
581 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
582 |
+
"metadata": {
|
583 |
+
"version": 1.0
|
584 |
+
}
|
585 |
+
},
|
586 |
+
"blimp_determiner_noun_agreement_1": {
|
587 |
+
"task": "blimp_determiner_noun_agreement_1",
|
588 |
+
"group": "blimp",
|
589 |
+
"dataset_path": "blimp",
|
590 |
+
"dataset_name": "determiner_noun_agreement_1",
|
591 |
+
"validation_split": "train",
|
592 |
+
"doc_to_text": "",
|
593 |
+
"doc_to_target": 0,
|
594 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
595 |
+
"description": "",
|
596 |
+
"target_delimiter": " ",
|
597 |
+
"fewshot_delimiter": "\n\n",
|
598 |
+
"num_fewshot": 0,
|
599 |
+
"metric_list": [
|
600 |
+
{
|
601 |
+
"metric": "acc"
|
602 |
+
}
|
603 |
+
],
|
604 |
+
"output_type": "multiple_choice",
|
605 |
+
"repeats": 1,
|
606 |
+
"should_decontaminate": true,
|
607 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
608 |
+
"metadata": {
|
609 |
+
"version": 1.0
|
610 |
+
}
|
611 |
+
},
|
612 |
+
"blimp_determiner_noun_agreement_2": {
|
613 |
+
"task": "blimp_determiner_noun_agreement_2",
|
614 |
+
"group": "blimp",
|
615 |
+
"dataset_path": "blimp",
|
616 |
+
"dataset_name": "determiner_noun_agreement_2",
|
617 |
+
"validation_split": "train",
|
618 |
+
"doc_to_text": "",
|
619 |
+
"doc_to_target": 0,
|
620 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
621 |
+
"description": "",
|
622 |
+
"target_delimiter": " ",
|
623 |
+
"fewshot_delimiter": "\n\n",
|
624 |
+
"num_fewshot": 0,
|
625 |
+
"metric_list": [
|
626 |
+
{
|
627 |
+
"metric": "acc"
|
628 |
+
}
|
629 |
+
],
|
630 |
+
"output_type": "multiple_choice",
|
631 |
+
"repeats": 1,
|
632 |
+
"should_decontaminate": true,
|
633 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
634 |
+
"metadata": {
|
635 |
+
"version": 1.0
|
636 |
+
}
|
637 |
+
},
|
638 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
639 |
+
"task": "blimp_determiner_noun_agreement_irregular_1",
|
640 |
+
"group": "blimp",
|
641 |
+
"dataset_path": "blimp",
|
642 |
+
"dataset_name": "determiner_noun_agreement_irregular_1",
|
643 |
+
"validation_split": "train",
|
644 |
+
"doc_to_text": "",
|
645 |
+
"doc_to_target": 0,
|
646 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
647 |
+
"description": "",
|
648 |
+
"target_delimiter": " ",
|
649 |
+
"fewshot_delimiter": "\n\n",
|
650 |
+
"num_fewshot": 0,
|
651 |
+
"metric_list": [
|
652 |
+
{
|
653 |
+
"metric": "acc"
|
654 |
+
}
|
655 |
+
],
|
656 |
+
"output_type": "multiple_choice",
|
657 |
+
"repeats": 1,
|
658 |
+
"should_decontaminate": true,
|
659 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
660 |
+
"metadata": {
|
661 |
+
"version": 1.0
|
662 |
+
}
|
663 |
+
},
|
664 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
665 |
+
"task": "blimp_determiner_noun_agreement_irregular_2",
|
666 |
+
"group": "blimp",
|
667 |
+
"dataset_path": "blimp",
|
668 |
+
"dataset_name": "determiner_noun_agreement_irregular_2",
|
669 |
+
"validation_split": "train",
|
670 |
+
"doc_to_text": "",
|
671 |
+
"doc_to_target": 0,
|
672 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
673 |
+
"description": "",
|
674 |
+
"target_delimiter": " ",
|
675 |
+
"fewshot_delimiter": "\n\n",
|
676 |
+
"num_fewshot": 0,
|
677 |
+
"metric_list": [
|
678 |
+
{
|
679 |
+
"metric": "acc"
|
680 |
+
}
|
681 |
+
],
|
682 |
+
"output_type": "multiple_choice",
|
683 |
+
"repeats": 1,
|
684 |
+
"should_decontaminate": true,
|
685 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
686 |
+
"metadata": {
|
687 |
+
"version": 1.0
|
688 |
+
}
|
689 |
+
},
|
690 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
691 |
+
"task": "blimp_determiner_noun_agreement_with_adj_2",
|
692 |
+
"group": "blimp",
|
693 |
+
"dataset_path": "blimp",
|
694 |
+
"dataset_name": "determiner_noun_agreement_with_adj_2",
|
695 |
+
"validation_split": "train",
|
696 |
+
"doc_to_text": "",
|
697 |
+
"doc_to_target": 0,
|
698 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
699 |
+
"description": "",
|
700 |
+
"target_delimiter": " ",
|
701 |
+
"fewshot_delimiter": "\n\n",
|
702 |
+
"num_fewshot": 0,
|
703 |
+
"metric_list": [
|
704 |
+
{
|
705 |
+
"metric": "acc"
|
706 |
+
}
|
707 |
+
],
|
708 |
+
"output_type": "multiple_choice",
|
709 |
+
"repeats": 1,
|
710 |
+
"should_decontaminate": true,
|
711 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
712 |
+
"metadata": {
|
713 |
+
"version": 1.0
|
714 |
+
}
|
715 |
+
},
|
716 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
717 |
+
"task": "blimp_determiner_noun_agreement_with_adj_irregular_1",
|
718 |
+
"group": "blimp",
|
719 |
+
"dataset_path": "blimp",
|
720 |
+
"dataset_name": "determiner_noun_agreement_with_adj_irregular_1",
|
721 |
+
"validation_split": "train",
|
722 |
+
"doc_to_text": "",
|
723 |
+
"doc_to_target": 0,
|
724 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
725 |
+
"description": "",
|
726 |
+
"target_delimiter": " ",
|
727 |
+
"fewshot_delimiter": "\n\n",
|
728 |
+
"num_fewshot": 0,
|
729 |
+
"metric_list": [
|
730 |
+
{
|
731 |
+
"metric": "acc"
|
732 |
+
}
|
733 |
+
],
|
734 |
+
"output_type": "multiple_choice",
|
735 |
+
"repeats": 1,
|
736 |
+
"should_decontaminate": true,
|
737 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
738 |
+
"metadata": {
|
739 |
+
"version": 1.0
|
740 |
+
}
|
741 |
+
},
|
742 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
743 |
+
"task": "blimp_determiner_noun_agreement_with_adj_irregular_2",
|
744 |
+
"group": "blimp",
|
745 |
+
"dataset_path": "blimp",
|
746 |
+
"dataset_name": "determiner_noun_agreement_with_adj_irregular_2",
|
747 |
+
"validation_split": "train",
|
748 |
+
"doc_to_text": "",
|
749 |
+
"doc_to_target": 0,
|
750 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
751 |
+
"description": "",
|
752 |
+
"target_delimiter": " ",
|
753 |
+
"fewshot_delimiter": "\n\n",
|
754 |
+
"num_fewshot": 0,
|
755 |
+
"metric_list": [
|
756 |
+
{
|
757 |
+
"metric": "acc"
|
758 |
+
}
|
759 |
+
],
|
760 |
+
"output_type": "multiple_choice",
|
761 |
+
"repeats": 1,
|
762 |
+
"should_decontaminate": true,
|
763 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
764 |
+
"metadata": {
|
765 |
+
"version": 1.0
|
766 |
+
}
|
767 |
+
},
|
768 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
769 |
+
"task": "blimp_determiner_noun_agreement_with_adjective_1",
|
770 |
+
"group": "blimp",
|
771 |
+
"dataset_path": "blimp",
|
772 |
+
"dataset_name": "determiner_noun_agreement_with_adjective_1",
|
773 |
+
"validation_split": "train",
|
774 |
+
"doc_to_text": "",
|
775 |
+
"doc_to_target": 0,
|
776 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
777 |
+
"description": "",
|
778 |
+
"target_delimiter": " ",
|
779 |
+
"fewshot_delimiter": "\n\n",
|
780 |
+
"num_fewshot": 0,
|
781 |
+
"metric_list": [
|
782 |
+
{
|
783 |
+
"metric": "acc"
|
784 |
+
}
|
785 |
+
],
|
786 |
+
"output_type": "multiple_choice",
|
787 |
+
"repeats": 1,
|
788 |
+
"should_decontaminate": true,
|
789 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
790 |
+
"metadata": {
|
791 |
+
"version": 1.0
|
792 |
+
}
|
793 |
+
},
|
794 |
+
"blimp_distractor_agreement_relational_noun": {
|
795 |
+
"task": "blimp_distractor_agreement_relational_noun",
|
796 |
+
"group": "blimp",
|
797 |
+
"dataset_path": "blimp",
|
798 |
+
"dataset_name": "distractor_agreement_relational_noun",
|
799 |
+
"validation_split": "train",
|
800 |
+
"doc_to_text": "",
|
801 |
+
"doc_to_target": 0,
|
802 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
803 |
+
"description": "",
|
804 |
+
"target_delimiter": " ",
|
805 |
+
"fewshot_delimiter": "\n\n",
|
806 |
+
"num_fewshot": 0,
|
807 |
+
"metric_list": [
|
808 |
+
{
|
809 |
+
"metric": "acc"
|
810 |
+
}
|
811 |
+
],
|
812 |
+
"output_type": "multiple_choice",
|
813 |
+
"repeats": 1,
|
814 |
+
"should_decontaminate": true,
|
815 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
816 |
+
"metadata": {
|
817 |
+
"version": 1.0
|
818 |
+
}
|
819 |
+
},
|
820 |
+
"blimp_distractor_agreement_relative_clause": {
|
821 |
+
"task": "blimp_distractor_agreement_relative_clause",
|
822 |
+
"group": "blimp",
|
823 |
+
"dataset_path": "blimp",
|
824 |
+
"dataset_name": "distractor_agreement_relative_clause",
|
825 |
+
"validation_split": "train",
|
826 |
+
"doc_to_text": "",
|
827 |
+
"doc_to_target": 0,
|
828 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
829 |
+
"description": "",
|
830 |
+
"target_delimiter": " ",
|
831 |
+
"fewshot_delimiter": "\n\n",
|
832 |
+
"num_fewshot": 0,
|
833 |
+
"metric_list": [
|
834 |
+
{
|
835 |
+
"metric": "acc"
|
836 |
+
}
|
837 |
+
],
|
838 |
+
"output_type": "multiple_choice",
|
839 |
+
"repeats": 1,
|
840 |
+
"should_decontaminate": true,
|
841 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
842 |
+
"metadata": {
|
843 |
+
"version": 1.0
|
844 |
+
}
|
845 |
+
},
|
846 |
+
"blimp_drop_argument": {
|
847 |
+
"task": "blimp_drop_argument",
|
848 |
+
"group": "blimp",
|
849 |
+
"dataset_path": "blimp",
|
850 |
+
"dataset_name": "drop_argument",
|
851 |
+
"validation_split": "train",
|
852 |
+
"doc_to_text": "",
|
853 |
+
"doc_to_target": 0,
|
854 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
855 |
+
"description": "",
|
856 |
+
"target_delimiter": " ",
|
857 |
+
"fewshot_delimiter": "\n\n",
|
858 |
+
"num_fewshot": 0,
|
859 |
+
"metric_list": [
|
860 |
+
{
|
861 |
+
"metric": "acc"
|
862 |
+
}
|
863 |
+
],
|
864 |
+
"output_type": "multiple_choice",
|
865 |
+
"repeats": 1,
|
866 |
+
"should_decontaminate": true,
|
867 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
868 |
+
"metadata": {
|
869 |
+
"version": 1.0
|
870 |
+
}
|
871 |
+
},
|
872 |
+
"blimp_ellipsis_n_bar_1": {
|
873 |
+
"task": "blimp_ellipsis_n_bar_1",
|
874 |
+
"group": "blimp",
|
875 |
+
"dataset_path": "blimp",
|
876 |
+
"dataset_name": "ellipsis_n_bar_1",
|
877 |
+
"validation_split": "train",
|
878 |
+
"doc_to_text": "",
|
879 |
+
"doc_to_target": 0,
|
880 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
881 |
+
"description": "",
|
882 |
+
"target_delimiter": " ",
|
883 |
+
"fewshot_delimiter": "\n\n",
|
884 |
+
"num_fewshot": 0,
|
885 |
+
"metric_list": [
|
886 |
+
{
|
887 |
+
"metric": "acc"
|
888 |
+
}
|
889 |
+
],
|
890 |
+
"output_type": "multiple_choice",
|
891 |
+
"repeats": 1,
|
892 |
+
"should_decontaminate": true,
|
893 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
894 |
+
"metadata": {
|
895 |
+
"version": 1.0
|
896 |
+
}
|
897 |
+
},
|
898 |
+
"blimp_ellipsis_n_bar_2": {
|
899 |
+
"task": "blimp_ellipsis_n_bar_2",
|
900 |
+
"group": "blimp",
|
901 |
+
"dataset_path": "blimp",
|
902 |
+
"dataset_name": "ellipsis_n_bar_2",
|
903 |
+
"validation_split": "train",
|
904 |
+
"doc_to_text": "",
|
905 |
+
"doc_to_target": 0,
|
906 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
907 |
+
"description": "",
|
908 |
+
"target_delimiter": " ",
|
909 |
+
"fewshot_delimiter": "\n\n",
|
910 |
+
"num_fewshot": 0,
|
911 |
+
"metric_list": [
|
912 |
+
{
|
913 |
+
"metric": "acc"
|
914 |
+
}
|
915 |
+
],
|
916 |
+
"output_type": "multiple_choice",
|
917 |
+
"repeats": 1,
|
918 |
+
"should_decontaminate": true,
|
919 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
920 |
+
"metadata": {
|
921 |
+
"version": 1.0
|
922 |
+
}
|
923 |
+
},
|
924 |
+
"blimp_existential_there_object_raising": {
|
925 |
+
"task": "blimp_existential_there_object_raising",
|
926 |
+
"group": "blimp",
|
927 |
+
"dataset_path": "blimp",
|
928 |
+
"dataset_name": "existential_there_object_raising",
|
929 |
+
"validation_split": "train",
|
930 |
+
"doc_to_text": "",
|
931 |
+
"doc_to_target": 0,
|
932 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
933 |
+
"description": "",
|
934 |
+
"target_delimiter": " ",
|
935 |
+
"fewshot_delimiter": "\n\n",
|
936 |
+
"num_fewshot": 0,
|
937 |
+
"metric_list": [
|
938 |
+
{
|
939 |
+
"metric": "acc"
|
940 |
+
}
|
941 |
+
],
|
942 |
+
"output_type": "multiple_choice",
|
943 |
+
"repeats": 1,
|
944 |
+
"should_decontaminate": true,
|
945 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
946 |
+
"metadata": {
|
947 |
+
"version": 1.0
|
948 |
+
}
|
949 |
+
},
|
950 |
+
"blimp_existential_there_quantifiers_1": {
|
951 |
+
"task": "blimp_existential_there_quantifiers_1",
|
952 |
+
"group": "blimp",
|
953 |
+
"dataset_path": "blimp",
|
954 |
+
"dataset_name": "existential_there_quantifiers_1",
|
955 |
+
"validation_split": "train",
|
956 |
+
"doc_to_text": "",
|
957 |
+
"doc_to_target": 0,
|
958 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
959 |
+
"description": "",
|
960 |
+
"target_delimiter": " ",
|
961 |
+
"fewshot_delimiter": "\n\n",
|
962 |
+
"num_fewshot": 0,
|
963 |
+
"metric_list": [
|
964 |
+
{
|
965 |
+
"metric": "acc"
|
966 |
+
}
|
967 |
+
],
|
968 |
+
"output_type": "multiple_choice",
|
969 |
+
"repeats": 1,
|
970 |
+
"should_decontaminate": true,
|
971 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
972 |
+
"metadata": {
|
973 |
+
"version": 1.0
|
974 |
+
}
|
975 |
+
},
|
976 |
+
"blimp_existential_there_quantifiers_2": {
|
977 |
+
"task": "blimp_existential_there_quantifiers_2",
|
978 |
+
"group": "blimp",
|
979 |
+
"dataset_path": "blimp",
|
980 |
+
"dataset_name": "existential_there_quantifiers_2",
|
981 |
+
"validation_split": "train",
|
982 |
+
"doc_to_text": "",
|
983 |
+
"doc_to_target": 0,
|
984 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
985 |
+
"description": "",
|
986 |
+
"target_delimiter": " ",
|
987 |
+
"fewshot_delimiter": "\n\n",
|
988 |
+
"num_fewshot": 0,
|
989 |
+
"metric_list": [
|
990 |
+
{
|
991 |
+
"metric": "acc"
|
992 |
+
}
|
993 |
+
],
|
994 |
+
"output_type": "multiple_choice",
|
995 |
+
"repeats": 1,
|
996 |
+
"should_decontaminate": true,
|
997 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
998 |
+
"metadata": {
|
999 |
+
"version": 1.0
|
1000 |
+
}
|
1001 |
+
},
|
1002 |
+
"blimp_existential_there_subject_raising": {
|
1003 |
+
"task": "blimp_existential_there_subject_raising",
|
1004 |
+
"group": "blimp",
|
1005 |
+
"dataset_path": "blimp",
|
1006 |
+
"dataset_name": "existential_there_subject_raising",
|
1007 |
+
"validation_split": "train",
|
1008 |
+
"doc_to_text": "",
|
1009 |
+
"doc_to_target": 0,
|
1010 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1011 |
+
"description": "",
|
1012 |
+
"target_delimiter": " ",
|
1013 |
+
"fewshot_delimiter": "\n\n",
|
1014 |
+
"num_fewshot": 0,
|
1015 |
+
"metric_list": [
|
1016 |
+
{
|
1017 |
+
"metric": "acc"
|
1018 |
+
}
|
1019 |
+
],
|
1020 |
+
"output_type": "multiple_choice",
|
1021 |
+
"repeats": 1,
|
1022 |
+
"should_decontaminate": true,
|
1023 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1024 |
+
"metadata": {
|
1025 |
+
"version": 1.0
|
1026 |
+
}
|
1027 |
+
},
|
1028 |
+
"blimp_expletive_it_object_raising": {
|
1029 |
+
"task": "blimp_expletive_it_object_raising",
|
1030 |
+
"group": "blimp",
|
1031 |
+
"dataset_path": "blimp",
|
1032 |
+
"dataset_name": "expletive_it_object_raising",
|
1033 |
+
"validation_split": "train",
|
1034 |
+
"doc_to_text": "",
|
1035 |
+
"doc_to_target": 0,
|
1036 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1037 |
+
"description": "",
|
1038 |
+
"target_delimiter": " ",
|
1039 |
+
"fewshot_delimiter": "\n\n",
|
1040 |
+
"num_fewshot": 0,
|
1041 |
+
"metric_list": [
|
1042 |
+
{
|
1043 |
+
"metric": "acc"
|
1044 |
+
}
|
1045 |
+
],
|
1046 |
+
"output_type": "multiple_choice",
|
1047 |
+
"repeats": 1,
|
1048 |
+
"should_decontaminate": true,
|
1049 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1050 |
+
"metadata": {
|
1051 |
+
"version": 1.0
|
1052 |
+
}
|
1053 |
+
},
|
1054 |
+
"blimp_inchoative": {
|
1055 |
+
"task": "blimp_inchoative",
|
1056 |
+
"group": "blimp",
|
1057 |
+
"dataset_path": "blimp",
|
1058 |
+
"dataset_name": "inchoative",
|
1059 |
+
"validation_split": "train",
|
1060 |
+
"doc_to_text": "",
|
1061 |
+
"doc_to_target": 0,
|
1062 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1063 |
+
"description": "",
|
1064 |
+
"target_delimiter": " ",
|
1065 |
+
"fewshot_delimiter": "\n\n",
|
1066 |
+
"num_fewshot": 0,
|
1067 |
+
"metric_list": [
|
1068 |
+
{
|
1069 |
+
"metric": "acc"
|
1070 |
+
}
|
1071 |
+
],
|
1072 |
+
"output_type": "multiple_choice",
|
1073 |
+
"repeats": 1,
|
1074 |
+
"should_decontaminate": true,
|
1075 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1076 |
+
"metadata": {
|
1077 |
+
"version": 1.0
|
1078 |
+
}
|
1079 |
+
},
|
1080 |
+
"blimp_intransitive": {
|
1081 |
+
"task": "blimp_intransitive",
|
1082 |
+
"group": "blimp",
|
1083 |
+
"dataset_path": "blimp",
|
1084 |
+
"dataset_name": "intransitive",
|
1085 |
+
"validation_split": "train",
|
1086 |
+
"doc_to_text": "",
|
1087 |
+
"doc_to_target": 0,
|
1088 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1089 |
+
"description": "",
|
1090 |
+
"target_delimiter": " ",
|
1091 |
+
"fewshot_delimiter": "\n\n",
|
1092 |
+
"num_fewshot": 0,
|
1093 |
+
"metric_list": [
|
1094 |
+
{
|
1095 |
+
"metric": "acc"
|
1096 |
+
}
|
1097 |
+
],
|
1098 |
+
"output_type": "multiple_choice",
|
1099 |
+
"repeats": 1,
|
1100 |
+
"should_decontaminate": true,
|
1101 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1102 |
+
"metadata": {
|
1103 |
+
"version": 1.0
|
1104 |
+
}
|
1105 |
+
},
|
1106 |
+
"blimp_irregular_past_participle_adjectives": {
|
1107 |
+
"task": "blimp_irregular_past_participle_adjectives",
|
1108 |
+
"group": "blimp",
|
1109 |
+
"dataset_path": "blimp",
|
1110 |
+
"dataset_name": "irregular_past_participle_adjectives",
|
1111 |
+
"validation_split": "train",
|
1112 |
+
"doc_to_text": "",
|
1113 |
+
"doc_to_target": 0,
|
1114 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1115 |
+
"description": "",
|
1116 |
+
"target_delimiter": " ",
|
1117 |
+
"fewshot_delimiter": "\n\n",
|
1118 |
+
"num_fewshot": 0,
|
1119 |
+
"metric_list": [
|
1120 |
+
{
|
1121 |
+
"metric": "acc"
|
1122 |
+
}
|
1123 |
+
],
|
1124 |
+
"output_type": "multiple_choice",
|
1125 |
+
"repeats": 1,
|
1126 |
+
"should_decontaminate": true,
|
1127 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1128 |
+
"metadata": {
|
1129 |
+
"version": 1.0
|
1130 |
+
}
|
1131 |
+
},
|
1132 |
+
"blimp_irregular_past_participle_verbs": {
|
1133 |
+
"task": "blimp_irregular_past_participle_verbs",
|
1134 |
+
"group": "blimp",
|
1135 |
+
"dataset_path": "blimp",
|
1136 |
+
"dataset_name": "irregular_past_participle_verbs",
|
1137 |
+
"validation_split": "train",
|
1138 |
+
"doc_to_text": "",
|
1139 |
+
"doc_to_target": 0,
|
1140 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1141 |
+
"description": "",
|
1142 |
+
"target_delimiter": " ",
|
1143 |
+
"fewshot_delimiter": "\n\n",
|
1144 |
+
"num_fewshot": 0,
|
1145 |
+
"metric_list": [
|
1146 |
+
{
|
1147 |
+
"metric": "acc"
|
1148 |
+
}
|
1149 |
+
],
|
1150 |
+
"output_type": "multiple_choice",
|
1151 |
+
"repeats": 1,
|
1152 |
+
"should_decontaminate": true,
|
1153 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1154 |
+
"metadata": {
|
1155 |
+
"version": 1.0
|
1156 |
+
}
|
1157 |
+
},
|
1158 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
1159 |
+
"task": "blimp_irregular_plural_subject_verb_agreement_1",
|
1160 |
+
"group": "blimp",
|
1161 |
+
"dataset_path": "blimp",
|
1162 |
+
"dataset_name": "irregular_plural_subject_verb_agreement_1",
|
1163 |
+
"validation_split": "train",
|
1164 |
+
"doc_to_text": "",
|
1165 |
+
"doc_to_target": 0,
|
1166 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1167 |
+
"description": "",
|
1168 |
+
"target_delimiter": " ",
|
1169 |
+
"fewshot_delimiter": "\n\n",
|
1170 |
+
"num_fewshot": 0,
|
1171 |
+
"metric_list": [
|
1172 |
+
{
|
1173 |
+
"metric": "acc"
|
1174 |
+
}
|
1175 |
+
],
|
1176 |
+
"output_type": "multiple_choice",
|
1177 |
+
"repeats": 1,
|
1178 |
+
"should_decontaminate": true,
|
1179 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1180 |
+
"metadata": {
|
1181 |
+
"version": 1.0
|
1182 |
+
}
|
1183 |
+
},
|
1184 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
1185 |
+
"task": "blimp_irregular_plural_subject_verb_agreement_2",
|
1186 |
+
"group": "blimp",
|
1187 |
+
"dataset_path": "blimp",
|
1188 |
+
"dataset_name": "irregular_plural_subject_verb_agreement_2",
|
1189 |
+
"validation_split": "train",
|
1190 |
+
"doc_to_text": "",
|
1191 |
+
"doc_to_target": 0,
|
1192 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1193 |
+
"description": "",
|
1194 |
+
"target_delimiter": " ",
|
1195 |
+
"fewshot_delimiter": "\n\n",
|
1196 |
+
"num_fewshot": 0,
|
1197 |
+
"metric_list": [
|
1198 |
+
{
|
1199 |
+
"metric": "acc"
|
1200 |
+
}
|
1201 |
+
],
|
1202 |
+
"output_type": "multiple_choice",
|
1203 |
+
"repeats": 1,
|
1204 |
+
"should_decontaminate": true,
|
1205 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1206 |
+
"metadata": {
|
1207 |
+
"version": 1.0
|
1208 |
+
}
|
1209 |
+
},
|
1210 |
+
"blimp_left_branch_island_echo_question": {
|
1211 |
+
"task": "blimp_left_branch_island_echo_question",
|
1212 |
+
"group": "blimp",
|
1213 |
+
"dataset_path": "blimp",
|
1214 |
+
"dataset_name": "left_branch_island_echo_question",
|
1215 |
+
"validation_split": "train",
|
1216 |
+
"doc_to_text": "",
|
1217 |
+
"doc_to_target": 0,
|
1218 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1219 |
+
"description": "",
|
1220 |
+
"target_delimiter": " ",
|
1221 |
+
"fewshot_delimiter": "\n\n",
|
1222 |
+
"num_fewshot": 0,
|
1223 |
+
"metric_list": [
|
1224 |
+
{
|
1225 |
+
"metric": "acc"
|
1226 |
+
}
|
1227 |
+
],
|
1228 |
+
"output_type": "multiple_choice",
|
1229 |
+
"repeats": 1,
|
1230 |
+
"should_decontaminate": true,
|
1231 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1232 |
+
"metadata": {
|
1233 |
+
"version": 1.0
|
1234 |
+
}
|
1235 |
+
},
|
1236 |
+
"blimp_left_branch_island_simple_question": {
|
1237 |
+
"task": "blimp_left_branch_island_simple_question",
|
1238 |
+
"group": "blimp",
|
1239 |
+
"dataset_path": "blimp",
|
1240 |
+
"dataset_name": "left_branch_island_simple_question",
|
1241 |
+
"validation_split": "train",
|
1242 |
+
"doc_to_text": "",
|
1243 |
+
"doc_to_target": 0,
|
1244 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1245 |
+
"description": "",
|
1246 |
+
"target_delimiter": " ",
|
1247 |
+
"fewshot_delimiter": "\n\n",
|
1248 |
+
"num_fewshot": 0,
|
1249 |
+
"metric_list": [
|
1250 |
+
{
|
1251 |
+
"metric": "acc"
|
1252 |
+
}
|
1253 |
+
],
|
1254 |
+
"output_type": "multiple_choice",
|
1255 |
+
"repeats": 1,
|
1256 |
+
"should_decontaminate": true,
|
1257 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1258 |
+
"metadata": {
|
1259 |
+
"version": 1.0
|
1260 |
+
}
|
1261 |
+
},
|
1262 |
+
"blimp_matrix_question_npi_licensor_present": {
|
1263 |
+
"task": "blimp_matrix_question_npi_licensor_present",
|
1264 |
+
"group": "blimp",
|
1265 |
+
"dataset_path": "blimp",
|
1266 |
+
"dataset_name": "matrix_question_npi_licensor_present",
|
1267 |
+
"validation_split": "train",
|
1268 |
+
"doc_to_text": "",
|
1269 |
+
"doc_to_target": 0,
|
1270 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1271 |
+
"description": "",
|
1272 |
+
"target_delimiter": " ",
|
1273 |
+
"fewshot_delimiter": "\n\n",
|
1274 |
+
"num_fewshot": 0,
|
1275 |
+
"metric_list": [
|
1276 |
+
{
|
1277 |
+
"metric": "acc"
|
1278 |
+
}
|
1279 |
+
],
|
1280 |
+
"output_type": "multiple_choice",
|
1281 |
+
"repeats": 1,
|
1282 |
+
"should_decontaminate": true,
|
1283 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1284 |
+
"metadata": {
|
1285 |
+
"version": 1.0
|
1286 |
+
}
|
1287 |
+
},
|
1288 |
+
"blimp_npi_present_1": {
|
1289 |
+
"task": "blimp_npi_present_1",
|
1290 |
+
"group": "blimp",
|
1291 |
+
"dataset_path": "blimp",
|
1292 |
+
"dataset_name": "npi_present_1",
|
1293 |
+
"validation_split": "train",
|
1294 |
+
"doc_to_text": "",
|
1295 |
+
"doc_to_target": 0,
|
1296 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1297 |
+
"description": "",
|
1298 |
+
"target_delimiter": " ",
|
1299 |
+
"fewshot_delimiter": "\n\n",
|
1300 |
+
"num_fewshot": 0,
|
1301 |
+
"metric_list": [
|
1302 |
+
{
|
1303 |
+
"metric": "acc"
|
1304 |
+
}
|
1305 |
+
],
|
1306 |
+
"output_type": "multiple_choice",
|
1307 |
+
"repeats": 1,
|
1308 |
+
"should_decontaminate": true,
|
1309 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1310 |
+
"metadata": {
|
1311 |
+
"version": 1.0
|
1312 |
+
}
|
1313 |
+
},
|
1314 |
+
"blimp_npi_present_2": {
|
1315 |
+
"task": "blimp_npi_present_2",
|
1316 |
+
"group": "blimp",
|
1317 |
+
"dataset_path": "blimp",
|
1318 |
+
"dataset_name": "npi_present_2",
|
1319 |
+
"validation_split": "train",
|
1320 |
+
"doc_to_text": "",
|
1321 |
+
"doc_to_target": 0,
|
1322 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1323 |
+
"description": "",
|
1324 |
+
"target_delimiter": " ",
|
1325 |
+
"fewshot_delimiter": "\n\n",
|
1326 |
+
"num_fewshot": 0,
|
1327 |
+
"metric_list": [
|
1328 |
+
{
|
1329 |
+
"metric": "acc"
|
1330 |
+
}
|
1331 |
+
],
|
1332 |
+
"output_type": "multiple_choice",
|
1333 |
+
"repeats": 1,
|
1334 |
+
"should_decontaminate": true,
|
1335 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1336 |
+
"metadata": {
|
1337 |
+
"version": 1.0
|
1338 |
+
}
|
1339 |
+
},
|
1340 |
+
"blimp_only_npi_licensor_present": {
|
1341 |
+
"task": "blimp_only_npi_licensor_present",
|
1342 |
+
"group": "blimp",
|
1343 |
+
"dataset_path": "blimp",
|
1344 |
+
"dataset_name": "only_npi_licensor_present",
|
1345 |
+
"validation_split": "train",
|
1346 |
+
"doc_to_text": "",
|
1347 |
+
"doc_to_target": 0,
|
1348 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1349 |
+
"description": "",
|
1350 |
+
"target_delimiter": " ",
|
1351 |
+
"fewshot_delimiter": "\n\n",
|
1352 |
+
"num_fewshot": 0,
|
1353 |
+
"metric_list": [
|
1354 |
+
{
|
1355 |
+
"metric": "acc"
|
1356 |
+
}
|
1357 |
+
],
|
1358 |
+
"output_type": "multiple_choice",
|
1359 |
+
"repeats": 1,
|
1360 |
+
"should_decontaminate": true,
|
1361 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1362 |
+
"metadata": {
|
1363 |
+
"version": 1.0
|
1364 |
+
}
|
1365 |
+
},
|
1366 |
+
"blimp_only_npi_scope": {
|
1367 |
+
"task": "blimp_only_npi_scope",
|
1368 |
+
"group": "blimp",
|
1369 |
+
"dataset_path": "blimp",
|
1370 |
+
"dataset_name": "only_npi_scope",
|
1371 |
+
"validation_split": "train",
|
1372 |
+
"doc_to_text": "",
|
1373 |
+
"doc_to_target": 0,
|
1374 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1375 |
+
"description": "",
|
1376 |
+
"target_delimiter": " ",
|
1377 |
+
"fewshot_delimiter": "\n\n",
|
1378 |
+
"num_fewshot": 0,
|
1379 |
+
"metric_list": [
|
1380 |
+
{
|
1381 |
+
"metric": "acc"
|
1382 |
+
}
|
1383 |
+
],
|
1384 |
+
"output_type": "multiple_choice",
|
1385 |
+
"repeats": 1,
|
1386 |
+
"should_decontaminate": true,
|
1387 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1388 |
+
"metadata": {
|
1389 |
+
"version": 1.0
|
1390 |
+
}
|
1391 |
+
},
|
1392 |
+
"blimp_passive_1": {
|
1393 |
+
"task": "blimp_passive_1",
|
1394 |
+
"group": "blimp",
|
1395 |
+
"dataset_path": "blimp",
|
1396 |
+
"dataset_name": "passive_1",
|
1397 |
+
"validation_split": "train",
|
1398 |
+
"doc_to_text": "",
|
1399 |
+
"doc_to_target": 0,
|
1400 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1401 |
+
"description": "",
|
1402 |
+
"target_delimiter": " ",
|
1403 |
+
"fewshot_delimiter": "\n\n",
|
1404 |
+
"num_fewshot": 0,
|
1405 |
+
"metric_list": [
|
1406 |
+
{
|
1407 |
+
"metric": "acc"
|
1408 |
+
}
|
1409 |
+
],
|
1410 |
+
"output_type": "multiple_choice",
|
1411 |
+
"repeats": 1,
|
1412 |
+
"should_decontaminate": true,
|
1413 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1414 |
+
"metadata": {
|
1415 |
+
"version": 1.0
|
1416 |
+
}
|
1417 |
+
},
|
1418 |
+
"blimp_passive_2": {
|
1419 |
+
"task": "blimp_passive_2",
|
1420 |
+
"group": "blimp",
|
1421 |
+
"dataset_path": "blimp",
|
1422 |
+
"dataset_name": "passive_2",
|
1423 |
+
"validation_split": "train",
|
1424 |
+
"doc_to_text": "",
|
1425 |
+
"doc_to_target": 0,
|
1426 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1427 |
+
"description": "",
|
1428 |
+
"target_delimiter": " ",
|
1429 |
+
"fewshot_delimiter": "\n\n",
|
1430 |
+
"num_fewshot": 0,
|
1431 |
+
"metric_list": [
|
1432 |
+
{
|
1433 |
+
"metric": "acc"
|
1434 |
+
}
|
1435 |
+
],
|
1436 |
+
"output_type": "multiple_choice",
|
1437 |
+
"repeats": 1,
|
1438 |
+
"should_decontaminate": true,
|
1439 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1440 |
+
"metadata": {
|
1441 |
+
"version": 1.0
|
1442 |
+
}
|
1443 |
+
},
|
1444 |
+
"blimp_principle_A_c_command": {
|
1445 |
+
"task": "blimp_principle_A_c_command",
|
1446 |
+
"group": "blimp",
|
1447 |
+
"dataset_path": "blimp",
|
1448 |
+
"dataset_name": "principle_A_c_command",
|
1449 |
+
"validation_split": "train",
|
1450 |
+
"doc_to_text": "",
|
1451 |
+
"doc_to_target": 0,
|
1452 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1453 |
+
"description": "",
|
1454 |
+
"target_delimiter": " ",
|
1455 |
+
"fewshot_delimiter": "\n\n",
|
1456 |
+
"num_fewshot": 0,
|
1457 |
+
"metric_list": [
|
1458 |
+
{
|
1459 |
+
"metric": "acc"
|
1460 |
+
}
|
1461 |
+
],
|
1462 |
+
"output_type": "multiple_choice",
|
1463 |
+
"repeats": 1,
|
1464 |
+
"should_decontaminate": true,
|
1465 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1466 |
+
"metadata": {
|
1467 |
+
"version": 1.0
|
1468 |
+
}
|
1469 |
+
},
|
1470 |
+
"blimp_principle_A_case_1": {
|
1471 |
+
"task": "blimp_principle_A_case_1",
|
1472 |
+
"group": "blimp",
|
1473 |
+
"dataset_path": "blimp",
|
1474 |
+
"dataset_name": "principle_A_case_1",
|
1475 |
+
"validation_split": "train",
|
1476 |
+
"doc_to_text": "",
|
1477 |
+
"doc_to_target": 0,
|
1478 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1479 |
+
"description": "",
|
1480 |
+
"target_delimiter": " ",
|
1481 |
+
"fewshot_delimiter": "\n\n",
|
1482 |
+
"num_fewshot": 0,
|
1483 |
+
"metric_list": [
|
1484 |
+
{
|
1485 |
+
"metric": "acc"
|
1486 |
+
}
|
1487 |
+
],
|
1488 |
+
"output_type": "multiple_choice",
|
1489 |
+
"repeats": 1,
|
1490 |
+
"should_decontaminate": true,
|
1491 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1492 |
+
"metadata": {
|
1493 |
+
"version": 1.0
|
1494 |
+
}
|
1495 |
+
},
|
1496 |
+
"blimp_principle_A_case_2": {
|
1497 |
+
"task": "blimp_principle_A_case_2",
|
1498 |
+
"group": "blimp",
|
1499 |
+
"dataset_path": "blimp",
|
1500 |
+
"dataset_name": "principle_A_case_2",
|
1501 |
+
"validation_split": "train",
|
1502 |
+
"doc_to_text": "",
|
1503 |
+
"doc_to_target": 0,
|
1504 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1505 |
+
"description": "",
|
1506 |
+
"target_delimiter": " ",
|
1507 |
+
"fewshot_delimiter": "\n\n",
|
1508 |
+
"num_fewshot": 0,
|
1509 |
+
"metric_list": [
|
1510 |
+
{
|
1511 |
+
"metric": "acc"
|
1512 |
+
}
|
1513 |
+
],
|
1514 |
+
"output_type": "multiple_choice",
|
1515 |
+
"repeats": 1,
|
1516 |
+
"should_decontaminate": true,
|
1517 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1518 |
+
"metadata": {
|
1519 |
+
"version": 1.0
|
1520 |
+
}
|
1521 |
+
},
|
1522 |
+
"blimp_principle_A_domain_1": {
|
1523 |
+
"task": "blimp_principle_A_domain_1",
|
1524 |
+
"group": "blimp",
|
1525 |
+
"dataset_path": "blimp",
|
1526 |
+
"dataset_name": "principle_A_domain_1",
|
1527 |
+
"validation_split": "train",
|
1528 |
+
"doc_to_text": "",
|
1529 |
+
"doc_to_target": 0,
|
1530 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1531 |
+
"description": "",
|
1532 |
+
"target_delimiter": " ",
|
1533 |
+
"fewshot_delimiter": "\n\n",
|
1534 |
+
"num_fewshot": 0,
|
1535 |
+
"metric_list": [
|
1536 |
+
{
|
1537 |
+
"metric": "acc"
|
1538 |
+
}
|
1539 |
+
],
|
1540 |
+
"output_type": "multiple_choice",
|
1541 |
+
"repeats": 1,
|
1542 |
+
"should_decontaminate": true,
|
1543 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1544 |
+
"metadata": {
|
1545 |
+
"version": 1.0
|
1546 |
+
}
|
1547 |
+
},
|
1548 |
+
"blimp_principle_A_domain_2": {
|
1549 |
+
"task": "blimp_principle_A_domain_2",
|
1550 |
+
"group": "blimp",
|
1551 |
+
"dataset_path": "blimp",
|
1552 |
+
"dataset_name": "principle_A_domain_2",
|
1553 |
+
"validation_split": "train",
|
1554 |
+
"doc_to_text": "",
|
1555 |
+
"doc_to_target": 0,
|
1556 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1557 |
+
"description": "",
|
1558 |
+
"target_delimiter": " ",
|
1559 |
+
"fewshot_delimiter": "\n\n",
|
1560 |
+
"num_fewshot": 0,
|
1561 |
+
"metric_list": [
|
1562 |
+
{
|
1563 |
+
"metric": "acc"
|
1564 |
+
}
|
1565 |
+
],
|
1566 |
+
"output_type": "multiple_choice",
|
1567 |
+
"repeats": 1,
|
1568 |
+
"should_decontaminate": true,
|
1569 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1570 |
+
"metadata": {
|
1571 |
+
"version": 1.0
|
1572 |
+
}
|
1573 |
+
},
|
1574 |
+
"blimp_principle_A_domain_3": {
|
1575 |
+
"task": "blimp_principle_A_domain_3",
|
1576 |
+
"group": "blimp",
|
1577 |
+
"dataset_path": "blimp",
|
1578 |
+
"dataset_name": "principle_A_domain_3",
|
1579 |
+
"validation_split": "train",
|
1580 |
+
"doc_to_text": "",
|
1581 |
+
"doc_to_target": 0,
|
1582 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1583 |
+
"description": "",
|
1584 |
+
"target_delimiter": " ",
|
1585 |
+
"fewshot_delimiter": "\n\n",
|
1586 |
+
"num_fewshot": 0,
|
1587 |
+
"metric_list": [
|
1588 |
+
{
|
1589 |
+
"metric": "acc"
|
1590 |
+
}
|
1591 |
+
],
|
1592 |
+
"output_type": "multiple_choice",
|
1593 |
+
"repeats": 1,
|
1594 |
+
"should_decontaminate": true,
|
1595 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1596 |
+
"metadata": {
|
1597 |
+
"version": 1.0
|
1598 |
+
}
|
1599 |
+
},
|
1600 |
+
"blimp_principle_A_reconstruction": {
|
1601 |
+
"task": "blimp_principle_A_reconstruction",
|
1602 |
+
"group": "blimp",
|
1603 |
+
"dataset_path": "blimp",
|
1604 |
+
"dataset_name": "principle_A_reconstruction",
|
1605 |
+
"validation_split": "train",
|
1606 |
+
"doc_to_text": "",
|
1607 |
+
"doc_to_target": 0,
|
1608 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1609 |
+
"description": "",
|
1610 |
+
"target_delimiter": " ",
|
1611 |
+
"fewshot_delimiter": "\n\n",
|
1612 |
+
"num_fewshot": 0,
|
1613 |
+
"metric_list": [
|
1614 |
+
{
|
1615 |
+
"metric": "acc"
|
1616 |
+
}
|
1617 |
+
],
|
1618 |
+
"output_type": "multiple_choice",
|
1619 |
+
"repeats": 1,
|
1620 |
+
"should_decontaminate": true,
|
1621 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1622 |
+
"metadata": {
|
1623 |
+
"version": 1.0
|
1624 |
+
}
|
1625 |
+
},
|
1626 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
1627 |
+
"task": "blimp_regular_plural_subject_verb_agreement_1",
|
1628 |
+
"group": "blimp",
|
1629 |
+
"dataset_path": "blimp",
|
1630 |
+
"dataset_name": "regular_plural_subject_verb_agreement_1",
|
1631 |
+
"validation_split": "train",
|
1632 |
+
"doc_to_text": "",
|
1633 |
+
"doc_to_target": 0,
|
1634 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1635 |
+
"description": "",
|
1636 |
+
"target_delimiter": " ",
|
1637 |
+
"fewshot_delimiter": "\n\n",
|
1638 |
+
"num_fewshot": 0,
|
1639 |
+
"metric_list": [
|
1640 |
+
{
|
1641 |
+
"metric": "acc"
|
1642 |
+
}
|
1643 |
+
],
|
1644 |
+
"output_type": "multiple_choice",
|
1645 |
+
"repeats": 1,
|
1646 |
+
"should_decontaminate": true,
|
1647 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1648 |
+
"metadata": {
|
1649 |
+
"version": 1.0
|
1650 |
+
}
|
1651 |
+
},
|
1652 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
1653 |
+
"task": "blimp_regular_plural_subject_verb_agreement_2",
|
1654 |
+
"group": "blimp",
|
1655 |
+
"dataset_path": "blimp",
|
1656 |
+
"dataset_name": "regular_plural_subject_verb_agreement_2",
|
1657 |
+
"validation_split": "train",
|
1658 |
+
"doc_to_text": "",
|
1659 |
+
"doc_to_target": 0,
|
1660 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1661 |
+
"description": "",
|
1662 |
+
"target_delimiter": " ",
|
1663 |
+
"fewshot_delimiter": "\n\n",
|
1664 |
+
"num_fewshot": 0,
|
1665 |
+
"metric_list": [
|
1666 |
+
{
|
1667 |
+
"metric": "acc"
|
1668 |
+
}
|
1669 |
+
],
|
1670 |
+
"output_type": "multiple_choice",
|
1671 |
+
"repeats": 1,
|
1672 |
+
"should_decontaminate": true,
|
1673 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1674 |
+
"metadata": {
|
1675 |
+
"version": 1.0
|
1676 |
+
}
|
1677 |
+
},
|
1678 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
1679 |
+
"task": "blimp_sentential_negation_npi_licensor_present",
|
1680 |
+
"group": "blimp",
|
1681 |
+
"dataset_path": "blimp",
|
1682 |
+
"dataset_name": "sentential_negation_npi_licensor_present",
|
1683 |
+
"validation_split": "train",
|
1684 |
+
"doc_to_text": "",
|
1685 |
+
"doc_to_target": 0,
|
1686 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1687 |
+
"description": "",
|
1688 |
+
"target_delimiter": " ",
|
1689 |
+
"fewshot_delimiter": "\n\n",
|
1690 |
+
"num_fewshot": 0,
|
1691 |
+
"metric_list": [
|
1692 |
+
{
|
1693 |
+
"metric": "acc"
|
1694 |
+
}
|
1695 |
+
],
|
1696 |
+
"output_type": "multiple_choice",
|
1697 |
+
"repeats": 1,
|
1698 |
+
"should_decontaminate": true,
|
1699 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1700 |
+
"metadata": {
|
1701 |
+
"version": 1.0
|
1702 |
+
}
|
1703 |
+
},
|
1704 |
+
"blimp_sentential_negation_npi_scope": {
|
1705 |
+
"task": "blimp_sentential_negation_npi_scope",
|
1706 |
+
"group": "blimp",
|
1707 |
+
"dataset_path": "blimp",
|
1708 |
+
"dataset_name": "sentential_negation_npi_scope",
|
1709 |
+
"validation_split": "train",
|
1710 |
+
"doc_to_text": "",
|
1711 |
+
"doc_to_target": 0,
|
1712 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1713 |
+
"description": "",
|
1714 |
+
"target_delimiter": " ",
|
1715 |
+
"fewshot_delimiter": "\n\n",
|
1716 |
+
"num_fewshot": 0,
|
1717 |
+
"metric_list": [
|
1718 |
+
{
|
1719 |
+
"metric": "acc"
|
1720 |
+
}
|
1721 |
+
],
|
1722 |
+
"output_type": "multiple_choice",
|
1723 |
+
"repeats": 1,
|
1724 |
+
"should_decontaminate": true,
|
1725 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1726 |
+
"metadata": {
|
1727 |
+
"version": 1.0
|
1728 |
+
}
|
1729 |
+
},
|
1730 |
+
"blimp_sentential_subject_island": {
|
1731 |
+
"task": "blimp_sentential_subject_island",
|
1732 |
+
"group": "blimp",
|
1733 |
+
"dataset_path": "blimp",
|
1734 |
+
"dataset_name": "sentential_subject_island",
|
1735 |
+
"validation_split": "train",
|
1736 |
+
"doc_to_text": "",
|
1737 |
+
"doc_to_target": 0,
|
1738 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1739 |
+
"description": "",
|
1740 |
+
"target_delimiter": " ",
|
1741 |
+
"fewshot_delimiter": "\n\n",
|
1742 |
+
"num_fewshot": 0,
|
1743 |
+
"metric_list": [
|
1744 |
+
{
|
1745 |
+
"metric": "acc"
|
1746 |
+
}
|
1747 |
+
],
|
1748 |
+
"output_type": "multiple_choice",
|
1749 |
+
"repeats": 1,
|
1750 |
+
"should_decontaminate": true,
|
1751 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1752 |
+
"metadata": {
|
1753 |
+
"version": 1.0
|
1754 |
+
}
|
1755 |
+
},
|
1756 |
+
"blimp_superlative_quantifiers_1": {
|
1757 |
+
"task": "blimp_superlative_quantifiers_1",
|
1758 |
+
"group": "blimp",
|
1759 |
+
"dataset_path": "blimp",
|
1760 |
+
"dataset_name": "superlative_quantifiers_1",
|
1761 |
+
"validation_split": "train",
|
1762 |
+
"doc_to_text": "",
|
1763 |
+
"doc_to_target": 0,
|
1764 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1765 |
+
"description": "",
|
1766 |
+
"target_delimiter": " ",
|
1767 |
+
"fewshot_delimiter": "\n\n",
|
1768 |
+
"num_fewshot": 0,
|
1769 |
+
"metric_list": [
|
1770 |
+
{
|
1771 |
+
"metric": "acc"
|
1772 |
+
}
|
1773 |
+
],
|
1774 |
+
"output_type": "multiple_choice",
|
1775 |
+
"repeats": 1,
|
1776 |
+
"should_decontaminate": true,
|
1777 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1778 |
+
"metadata": {
|
1779 |
+
"version": 1.0
|
1780 |
+
}
|
1781 |
+
},
|
1782 |
+
"blimp_superlative_quantifiers_2": {
|
1783 |
+
"task": "blimp_superlative_quantifiers_2",
|
1784 |
+
"group": "blimp",
|
1785 |
+
"dataset_path": "blimp",
|
1786 |
+
"dataset_name": "superlative_quantifiers_2",
|
1787 |
+
"validation_split": "train",
|
1788 |
+
"doc_to_text": "",
|
1789 |
+
"doc_to_target": 0,
|
1790 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1791 |
+
"description": "",
|
1792 |
+
"target_delimiter": " ",
|
1793 |
+
"fewshot_delimiter": "\n\n",
|
1794 |
+
"num_fewshot": 0,
|
1795 |
+
"metric_list": [
|
1796 |
+
{
|
1797 |
+
"metric": "acc"
|
1798 |
+
}
|
1799 |
+
],
|
1800 |
+
"output_type": "multiple_choice",
|
1801 |
+
"repeats": 1,
|
1802 |
+
"should_decontaminate": true,
|
1803 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1804 |
+
"metadata": {
|
1805 |
+
"version": 1.0
|
1806 |
+
}
|
1807 |
+
},
|
1808 |
+
"blimp_tough_vs_raising_1": {
|
1809 |
+
"task": "blimp_tough_vs_raising_1",
|
1810 |
+
"group": "blimp",
|
1811 |
+
"dataset_path": "blimp",
|
1812 |
+
"dataset_name": "tough_vs_raising_1",
|
1813 |
+
"validation_split": "train",
|
1814 |
+
"doc_to_text": "",
|
1815 |
+
"doc_to_target": 0,
|
1816 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1817 |
+
"description": "",
|
1818 |
+
"target_delimiter": " ",
|
1819 |
+
"fewshot_delimiter": "\n\n",
|
1820 |
+
"num_fewshot": 0,
|
1821 |
+
"metric_list": [
|
1822 |
+
{
|
1823 |
+
"metric": "acc"
|
1824 |
+
}
|
1825 |
+
],
|
1826 |
+
"output_type": "multiple_choice",
|
1827 |
+
"repeats": 1,
|
1828 |
+
"should_decontaminate": true,
|
1829 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1830 |
+
"metadata": {
|
1831 |
+
"version": 1.0
|
1832 |
+
}
|
1833 |
+
},
|
1834 |
+
"blimp_tough_vs_raising_2": {
|
1835 |
+
"task": "blimp_tough_vs_raising_2",
|
1836 |
+
"group": "blimp",
|
1837 |
+
"dataset_path": "blimp",
|
1838 |
+
"dataset_name": "tough_vs_raising_2",
|
1839 |
+
"validation_split": "train",
|
1840 |
+
"doc_to_text": "",
|
1841 |
+
"doc_to_target": 0,
|
1842 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1843 |
+
"description": "",
|
1844 |
+
"target_delimiter": " ",
|
1845 |
+
"fewshot_delimiter": "\n\n",
|
1846 |
+
"num_fewshot": 0,
|
1847 |
+
"metric_list": [
|
1848 |
+
{
|
1849 |
+
"metric": "acc"
|
1850 |
+
}
|
1851 |
+
],
|
1852 |
+
"output_type": "multiple_choice",
|
1853 |
+
"repeats": 1,
|
1854 |
+
"should_decontaminate": true,
|
1855 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1856 |
+
"metadata": {
|
1857 |
+
"version": 1.0
|
1858 |
+
}
|
1859 |
+
},
|
1860 |
+
"blimp_transitive": {
|
1861 |
+
"task": "blimp_transitive",
|
1862 |
+
"group": "blimp",
|
1863 |
+
"dataset_path": "blimp",
|
1864 |
+
"dataset_name": "transitive",
|
1865 |
+
"validation_split": "train",
|
1866 |
+
"doc_to_text": "",
|
1867 |
+
"doc_to_target": 0,
|
1868 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1869 |
+
"description": "",
|
1870 |
+
"target_delimiter": " ",
|
1871 |
+
"fewshot_delimiter": "\n\n",
|
1872 |
+
"num_fewshot": 0,
|
1873 |
+
"metric_list": [
|
1874 |
+
{
|
1875 |
+
"metric": "acc"
|
1876 |
+
}
|
1877 |
+
],
|
1878 |
+
"output_type": "multiple_choice",
|
1879 |
+
"repeats": 1,
|
1880 |
+
"should_decontaminate": true,
|
1881 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1882 |
+
"metadata": {
|
1883 |
+
"version": 1.0
|
1884 |
+
}
|
1885 |
+
},
|
1886 |
+
"blimp_wh_island": {
|
1887 |
+
"task": "blimp_wh_island",
|
1888 |
+
"group": "blimp",
|
1889 |
+
"dataset_path": "blimp",
|
1890 |
+
"dataset_name": "wh_island",
|
1891 |
+
"validation_split": "train",
|
1892 |
+
"doc_to_text": "",
|
1893 |
+
"doc_to_target": 0,
|
1894 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1895 |
+
"description": "",
|
1896 |
+
"target_delimiter": " ",
|
1897 |
+
"fewshot_delimiter": "\n\n",
|
1898 |
+
"num_fewshot": 0,
|
1899 |
+
"metric_list": [
|
1900 |
+
{
|
1901 |
+
"metric": "acc"
|
1902 |
+
}
|
1903 |
+
],
|
1904 |
+
"output_type": "multiple_choice",
|
1905 |
+
"repeats": 1,
|
1906 |
+
"should_decontaminate": true,
|
1907 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1908 |
+
"metadata": {
|
1909 |
+
"version": 1.0
|
1910 |
+
}
|
1911 |
+
},
|
1912 |
+
"blimp_wh_questions_object_gap": {
|
1913 |
+
"task": "blimp_wh_questions_object_gap",
|
1914 |
+
"group": "blimp",
|
1915 |
+
"dataset_path": "blimp",
|
1916 |
+
"dataset_name": "wh_questions_object_gap",
|
1917 |
+
"validation_split": "train",
|
1918 |
+
"doc_to_text": "",
|
1919 |
+
"doc_to_target": 0,
|
1920 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1921 |
+
"description": "",
|
1922 |
+
"target_delimiter": " ",
|
1923 |
+
"fewshot_delimiter": "\n\n",
|
1924 |
+
"num_fewshot": 0,
|
1925 |
+
"metric_list": [
|
1926 |
+
{
|
1927 |
+
"metric": "acc"
|
1928 |
+
}
|
1929 |
+
],
|
1930 |
+
"output_type": "multiple_choice",
|
1931 |
+
"repeats": 1,
|
1932 |
+
"should_decontaminate": true,
|
1933 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1934 |
+
"metadata": {
|
1935 |
+
"version": 1.0
|
1936 |
+
}
|
1937 |
+
},
|
1938 |
+
"blimp_wh_questions_subject_gap": {
|
1939 |
+
"task": "blimp_wh_questions_subject_gap",
|
1940 |
+
"group": "blimp",
|
1941 |
+
"dataset_path": "blimp",
|
1942 |
+
"dataset_name": "wh_questions_subject_gap",
|
1943 |
+
"validation_split": "train",
|
1944 |
+
"doc_to_text": "",
|
1945 |
+
"doc_to_target": 0,
|
1946 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1947 |
+
"description": "",
|
1948 |
+
"target_delimiter": " ",
|
1949 |
+
"fewshot_delimiter": "\n\n",
|
1950 |
+
"num_fewshot": 0,
|
1951 |
+
"metric_list": [
|
1952 |
+
{
|
1953 |
+
"metric": "acc"
|
1954 |
+
}
|
1955 |
+
],
|
1956 |
+
"output_type": "multiple_choice",
|
1957 |
+
"repeats": 1,
|
1958 |
+
"should_decontaminate": true,
|
1959 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1960 |
+
"metadata": {
|
1961 |
+
"version": 1.0
|
1962 |
+
}
|
1963 |
+
},
|
1964 |
+
"blimp_wh_questions_subject_gap_long_distance": {
|
1965 |
+
"task": "blimp_wh_questions_subject_gap_long_distance",
|
1966 |
+
"group": "blimp",
|
1967 |
+
"dataset_path": "blimp",
|
1968 |
+
"dataset_name": "wh_questions_subject_gap_long_distance",
|
1969 |
+
"validation_split": "train",
|
1970 |
+
"doc_to_text": "",
|
1971 |
+
"doc_to_target": 0,
|
1972 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1973 |
+
"description": "",
|
1974 |
+
"target_delimiter": " ",
|
1975 |
+
"fewshot_delimiter": "\n\n",
|
1976 |
+
"num_fewshot": 0,
|
1977 |
+
"metric_list": [
|
1978 |
+
{
|
1979 |
+
"metric": "acc"
|
1980 |
+
}
|
1981 |
+
],
|
1982 |
+
"output_type": "multiple_choice",
|
1983 |
+
"repeats": 1,
|
1984 |
+
"should_decontaminate": true,
|
1985 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1986 |
+
"metadata": {
|
1987 |
+
"version": 1.0
|
1988 |
+
}
|
1989 |
+
},
|
1990 |
+
"blimp_wh_vs_that_no_gap": {
|
1991 |
+
"task": "blimp_wh_vs_that_no_gap",
|
1992 |
+
"group": "blimp",
|
1993 |
+
"dataset_path": "blimp",
|
1994 |
+
"dataset_name": "wh_vs_that_no_gap",
|
1995 |
+
"validation_split": "train",
|
1996 |
+
"doc_to_text": "",
|
1997 |
+
"doc_to_target": 0,
|
1998 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1999 |
+
"description": "",
|
2000 |
+
"target_delimiter": " ",
|
2001 |
+
"fewshot_delimiter": "\n\n",
|
2002 |
+
"num_fewshot": 0,
|
2003 |
+
"metric_list": [
|
2004 |
+
{
|
2005 |
+
"metric": "acc"
|
2006 |
+
}
|
2007 |
+
],
|
2008 |
+
"output_type": "multiple_choice",
|
2009 |
+
"repeats": 1,
|
2010 |
+
"should_decontaminate": true,
|
2011 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2012 |
+
"metadata": {
|
2013 |
+
"version": 1.0
|
2014 |
+
}
|
2015 |
+
},
|
2016 |
+
"blimp_wh_vs_that_no_gap_long_distance": {
|
2017 |
+
"task": "blimp_wh_vs_that_no_gap_long_distance",
|
2018 |
+
"group": "blimp",
|
2019 |
+
"dataset_path": "blimp",
|
2020 |
+
"dataset_name": "wh_vs_that_no_gap_long_distance",
|
2021 |
+
"validation_split": "train",
|
2022 |
+
"doc_to_text": "",
|
2023 |
+
"doc_to_target": 0,
|
2024 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2025 |
+
"description": "",
|
2026 |
+
"target_delimiter": " ",
|
2027 |
+
"fewshot_delimiter": "\n\n",
|
2028 |
+
"num_fewshot": 0,
|
2029 |
+
"metric_list": [
|
2030 |
+
{
|
2031 |
+
"metric": "acc"
|
2032 |
+
}
|
2033 |
+
],
|
2034 |
+
"output_type": "multiple_choice",
|
2035 |
+
"repeats": 1,
|
2036 |
+
"should_decontaminate": true,
|
2037 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2038 |
+
"metadata": {
|
2039 |
+
"version": 1.0
|
2040 |
+
}
|
2041 |
+
},
|
2042 |
+
"blimp_wh_vs_that_with_gap": {
|
2043 |
+
"task": "blimp_wh_vs_that_with_gap",
|
2044 |
+
"group": "blimp",
|
2045 |
+
"dataset_path": "blimp",
|
2046 |
+
"dataset_name": "wh_vs_that_with_gap",
|
2047 |
+
"validation_split": "train",
|
2048 |
+
"doc_to_text": "",
|
2049 |
+
"doc_to_target": 0,
|
2050 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2051 |
+
"description": "",
|
2052 |
+
"target_delimiter": " ",
|
2053 |
+
"fewshot_delimiter": "\n\n",
|
2054 |
+
"num_fewshot": 0,
|
2055 |
+
"metric_list": [
|
2056 |
+
{
|
2057 |
+
"metric": "acc"
|
2058 |
+
}
|
2059 |
+
],
|
2060 |
+
"output_type": "multiple_choice",
|
2061 |
+
"repeats": 1,
|
2062 |
+
"should_decontaminate": true,
|
2063 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2064 |
+
"metadata": {
|
2065 |
+
"version": 1.0
|
2066 |
+
}
|
2067 |
+
},
|
2068 |
+
"blimp_wh_vs_that_with_gap_long_distance": {
|
2069 |
+
"task": "blimp_wh_vs_that_with_gap_long_distance",
|
2070 |
+
"group": "blimp",
|
2071 |
+
"dataset_path": "blimp",
|
2072 |
+
"dataset_name": "wh_vs_that_with_gap_long_distance",
|
2073 |
+
"validation_split": "train",
|
2074 |
+
"doc_to_text": "",
|
2075 |
+
"doc_to_target": 0,
|
2076 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2077 |
+
"description": "",
|
2078 |
+
"target_delimiter": " ",
|
2079 |
+
"fewshot_delimiter": "\n\n",
|
2080 |
+
"num_fewshot": 0,
|
2081 |
+
"metric_list": [
|
2082 |
+
{
|
2083 |
+
"metric": "acc"
|
2084 |
+
}
|
2085 |
+
],
|
2086 |
+
"output_type": "multiple_choice",
|
2087 |
+
"repeats": 1,
|
2088 |
+
"should_decontaminate": true,
|
2089 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2090 |
+
"metadata": {
|
2091 |
+
"version": 1.0
|
2092 |
+
}
|
2093 |
+
}
|
2094 |
+
},
|
2095 |
+
"versions": {
|
2096 |
+
"blimp": "N/A",
|
2097 |
+
"blimp_adjunct_island": 1.0,
|
2098 |
+
"blimp_anaphor_gender_agreement": 1.0,
|
2099 |
+
"blimp_anaphor_number_agreement": 1.0,
|
2100 |
+
"blimp_animate_subject_passive": 1.0,
|
2101 |
+
"blimp_animate_subject_trans": 1.0,
|
2102 |
+
"blimp_causative": 1.0,
|
2103 |
+
"blimp_complex_NP_island": 1.0,
|
2104 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": 1.0,
|
2105 |
+
"blimp_coordinate_structure_constraint_object_extraction": 1.0,
|
2106 |
+
"blimp_determiner_noun_agreement_1": 1.0,
|
2107 |
+
"blimp_determiner_noun_agreement_2": 1.0,
|
2108 |
+
"blimp_determiner_noun_agreement_irregular_1": 1.0,
|
2109 |
+
"blimp_determiner_noun_agreement_irregular_2": 1.0,
|
2110 |
+
"blimp_determiner_noun_agreement_with_adj_2": 1.0,
|
2111 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": 1.0,
|
2112 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": 1.0,
|
2113 |
+
"blimp_determiner_noun_agreement_with_adjective_1": 1.0,
|
2114 |
+
"blimp_distractor_agreement_relational_noun": 1.0,
|
2115 |
+
"blimp_distractor_agreement_relative_clause": 1.0,
|
2116 |
+
"blimp_drop_argument": 1.0,
|
2117 |
+
"blimp_ellipsis_n_bar_1": 1.0,
|
2118 |
+
"blimp_ellipsis_n_bar_2": 1.0,
|
2119 |
+
"blimp_existential_there_object_raising": 1.0,
|
2120 |
+
"blimp_existential_there_quantifiers_1": 1.0,
|
2121 |
+
"blimp_existential_there_quantifiers_2": 1.0,
|
2122 |
+
"blimp_existential_there_subject_raising": 1.0,
|
2123 |
+
"blimp_expletive_it_object_raising": 1.0,
|
2124 |
+
"blimp_inchoative": 1.0,
|
2125 |
+
"blimp_intransitive": 1.0,
|
2126 |
+
"blimp_irregular_past_participle_adjectives": 1.0,
|
2127 |
+
"blimp_irregular_past_participle_verbs": 1.0,
|
2128 |
+
"blimp_irregular_plural_subject_verb_agreement_1": 1.0,
|
2129 |
+
"blimp_irregular_plural_subject_verb_agreement_2": 1.0,
|
2130 |
+
"blimp_left_branch_island_echo_question": 1.0,
|
2131 |
+
"blimp_left_branch_island_simple_question": 1.0,
|
2132 |
+
"blimp_matrix_question_npi_licensor_present": 1.0,
|
2133 |
+
"blimp_npi_present_1": 1.0,
|
2134 |
+
"blimp_npi_present_2": 1.0,
|
2135 |
+
"blimp_only_npi_licensor_present": 1.0,
|
2136 |
+
"blimp_only_npi_scope": 1.0,
|
2137 |
+
"blimp_passive_1": 1.0,
|
2138 |
+
"blimp_passive_2": 1.0,
|
2139 |
+
"blimp_principle_A_c_command": 1.0,
|
2140 |
+
"blimp_principle_A_case_1": 1.0,
|
2141 |
+
"blimp_principle_A_case_2": 1.0,
|
2142 |
+
"blimp_principle_A_domain_1": 1.0,
|
2143 |
+
"blimp_principle_A_domain_2": 1.0,
|
2144 |
+
"blimp_principle_A_domain_3": 1.0,
|
2145 |
+
"blimp_principle_A_reconstruction": 1.0,
|
2146 |
+
"blimp_regular_plural_subject_verb_agreement_1": 1.0,
|
2147 |
+
"blimp_regular_plural_subject_verb_agreement_2": 1.0,
|
2148 |
+
"blimp_sentential_negation_npi_licensor_present": 1.0,
|
2149 |
+
"blimp_sentential_negation_npi_scope": 1.0,
|
2150 |
+
"blimp_sentential_subject_island": 1.0,
|
2151 |
+
"blimp_superlative_quantifiers_1": 1.0,
|
2152 |
+
"blimp_superlative_quantifiers_2": 1.0,
|
2153 |
+
"blimp_tough_vs_raising_1": 1.0,
|
2154 |
+
"blimp_tough_vs_raising_2": 1.0,
|
2155 |
+
"blimp_transitive": 1.0,
|
2156 |
+
"blimp_wh_island": 1.0,
|
2157 |
+
"blimp_wh_questions_object_gap": 1.0,
|
2158 |
+
"blimp_wh_questions_subject_gap": 1.0,
|
2159 |
+
"blimp_wh_questions_subject_gap_long_distance": 1.0,
|
2160 |
+
"blimp_wh_vs_that_no_gap": 1.0,
|
2161 |
+
"blimp_wh_vs_that_no_gap_long_distance": 1.0,
|
2162 |
+
"blimp_wh_vs_that_with_gap": 1.0,
|
2163 |
+
"blimp_wh_vs_that_with_gap_long_distance": 1.0
|
2164 |
+
},
|
2165 |
+
"n-shot": {
|
2166 |
+
"blimp": 0,
|
2167 |
+
"blimp_adjunct_island": 0,
|
2168 |
+
"blimp_anaphor_gender_agreement": 0,
|
2169 |
+
"blimp_anaphor_number_agreement": 0,
|
2170 |
+
"blimp_animate_subject_passive": 0,
|
2171 |
+
"blimp_animate_subject_trans": 0,
|
2172 |
+
"blimp_causative": 0,
|
2173 |
+
"blimp_complex_NP_island": 0,
|
2174 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": 0,
|
2175 |
+
"blimp_coordinate_structure_constraint_object_extraction": 0,
|
2176 |
+
"blimp_determiner_noun_agreement_1": 0,
|
2177 |
+
"blimp_determiner_noun_agreement_2": 0,
|
2178 |
+
"blimp_determiner_noun_agreement_irregular_1": 0,
|
2179 |
+
"blimp_determiner_noun_agreement_irregular_2": 0,
|
2180 |
+
"blimp_determiner_noun_agreement_with_adj_2": 0,
|
2181 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": 0,
|
2182 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": 0,
|
2183 |
+
"blimp_determiner_noun_agreement_with_adjective_1": 0,
|
2184 |
+
"blimp_distractor_agreement_relational_noun": 0,
|
2185 |
+
"blimp_distractor_agreement_relative_clause": 0,
|
2186 |
+
"blimp_drop_argument": 0,
|
2187 |
+
"blimp_ellipsis_n_bar_1": 0,
|
2188 |
+
"blimp_ellipsis_n_bar_2": 0,
|
2189 |
+
"blimp_existential_there_object_raising": 0,
|
2190 |
+
"blimp_existential_there_quantifiers_1": 0,
|
2191 |
+
"blimp_existential_there_quantifiers_2": 0,
|
2192 |
+
"blimp_existential_there_subject_raising": 0,
|
2193 |
+
"blimp_expletive_it_object_raising": 0,
|
2194 |
+
"blimp_inchoative": 0,
|
2195 |
+
"blimp_intransitive": 0,
|
2196 |
+
"blimp_irregular_past_participle_adjectives": 0,
|
2197 |
+
"blimp_irregular_past_participle_verbs": 0,
|
2198 |
+
"blimp_irregular_plural_subject_verb_agreement_1": 0,
|
2199 |
+
"blimp_irregular_plural_subject_verb_agreement_2": 0,
|
2200 |
+
"blimp_left_branch_island_echo_question": 0,
|
2201 |
+
"blimp_left_branch_island_simple_question": 0,
|
2202 |
+
"blimp_matrix_question_npi_licensor_present": 0,
|
2203 |
+
"blimp_npi_present_1": 0,
|
2204 |
+
"blimp_npi_present_2": 0,
|
2205 |
+
"blimp_only_npi_licensor_present": 0,
|
2206 |
+
"blimp_only_npi_scope": 0,
|
2207 |
+
"blimp_passive_1": 0,
|
2208 |
+
"blimp_passive_2": 0,
|
2209 |
+
"blimp_principle_A_c_command": 0,
|
2210 |
+
"blimp_principle_A_case_1": 0,
|
2211 |
+
"blimp_principle_A_case_2": 0,
|
2212 |
+
"blimp_principle_A_domain_1": 0,
|
2213 |
+
"blimp_principle_A_domain_2": 0,
|
2214 |
+
"blimp_principle_A_domain_3": 0,
|
2215 |
+
"blimp_principle_A_reconstruction": 0,
|
2216 |
+
"blimp_regular_plural_subject_verb_agreement_1": 0,
|
2217 |
+
"blimp_regular_plural_subject_verb_agreement_2": 0,
|
2218 |
+
"blimp_sentential_negation_npi_licensor_present": 0,
|
2219 |
+
"blimp_sentential_negation_npi_scope": 0,
|
2220 |
+
"blimp_sentential_subject_island": 0,
|
2221 |
+
"blimp_superlative_quantifiers_1": 0,
|
2222 |
+
"blimp_superlative_quantifiers_2": 0,
|
2223 |
+
"blimp_tough_vs_raising_1": 0,
|
2224 |
+
"blimp_tough_vs_raising_2": 0,
|
2225 |
+
"blimp_transitive": 0,
|
2226 |
+
"blimp_wh_island": 0,
|
2227 |
+
"blimp_wh_questions_object_gap": 0,
|
2228 |
+
"blimp_wh_questions_subject_gap": 0,
|
2229 |
+
"blimp_wh_questions_subject_gap_long_distance": 0,
|
2230 |
+
"blimp_wh_vs_that_no_gap": 0,
|
2231 |
+
"blimp_wh_vs_that_no_gap_long_distance": 0,
|
2232 |
+
"blimp_wh_vs_that_with_gap": 0,
|
2233 |
+
"blimp_wh_vs_that_with_gap_long_distance": 0
|
2234 |
+
},
|
2235 |
+
"config": {
|
2236 |
+
"model": "hf",
|
2237 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
2238 |
+
"batch_size": "auto",
|
2239 |
+
"batch_sizes": [
|
2240 |
+
64
|
2241 |
+
],
|
2242 |
+
"device": null,
|
2243 |
+
"use_cache": null,
|
2244 |
+
"limit": null,
|
2245 |
+
"bootstrap_iters": 100000,
|
2246 |
+
"gen_kwargs": null
|
2247 |
+
},
|
2248 |
+
"git_hash": "1ee41f7"
|
2249 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:89cbb54498164135945ff3ae30ba8b91824a5e591209160a6134abba241f273c
|
3 |
+
size 318042
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ce5b0b9ddb9bb2a2f58452a6384f7e7a0172502934d1dcc703a282fbc958f876
|
3 |
+
size 148126
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"copa": {
|
4 |
+
"acc,none": 0.76,
|
5 |
+
"acc_stderr,none": 0.04292346959909284,
|
6 |
+
"alias": "copa"
|
7 |
+
}
|
8 |
+
},
|
9 |
+
"configs": {
|
10 |
+
"copa": {
|
11 |
+
"task": "copa",
|
12 |
+
"group": [
|
13 |
+
"super-glue-lm-eval-v1"
|
14 |
+
],
|
15 |
+
"dataset_path": "super_glue",
|
16 |
+
"dataset_name": "copa",
|
17 |
+
"training_split": "train",
|
18 |
+
"validation_split": "validation",
|
19 |
+
"doc_to_text": "def doc_to_text(doc):\n # Drop the period\n connector = {\n \"cause\": \"because\",\n \"effect\": \"therefore\",\n }[doc[\"question\"]]\n return doc[\"premise\"].strip()[:-1] + f\" {connector}\"\n",
|
20 |
+
"doc_to_target": "def doc_to_target(doc):\n correct_choice = doc[\"choice1\"] if doc[\"label\"] == 0 else doc[\"choice2\"]\n # Connect the sentences\n return \" \" + convert_choice(correct_choice)\n",
|
21 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [\" \" + convert_choice(doc[\"choice1\"]), \" \" + convert_choice(doc[\"choice2\"])]\n",
|
22 |
+
"description": "",
|
23 |
+
"target_delimiter": " ",
|
24 |
+
"fewshot_delimiter": "\n\n",
|
25 |
+
"metric_list": [
|
26 |
+
{
|
27 |
+
"metric": "acc"
|
28 |
+
}
|
29 |
+
],
|
30 |
+
"output_type": "multiple_choice",
|
31 |
+
"repeats": 1,
|
32 |
+
"should_decontaminate": false,
|
33 |
+
"metadata": {
|
34 |
+
"version": 1.0
|
35 |
+
}
|
36 |
+
}
|
37 |
+
},
|
38 |
+
"versions": {
|
39 |
+
"copa": 1.0
|
40 |
+
},
|
41 |
+
"n-shot": {
|
42 |
+
"copa": 0
|
43 |
+
},
|
44 |
+
"config": {
|
45 |
+
"model": "hf",
|
46 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
47 |
+
"batch_size": "auto",
|
48 |
+
"batch_sizes": [
|
49 |
+
64
|
50 |
+
],
|
51 |
+
"device": null,
|
52 |
+
"use_cache": null,
|
53 |
+
"limit": null,
|
54 |
+
"bootstrap_iters": 100000,
|
55 |
+
"gen_kwargs": null
|
56 |
+
},
|
57 |
+
"git_hash": "1ee41f7"
|
58 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b89275fc3520f76c9d199d3f7145a423401188faecfaf1e51cb9de291445fbf0
|
3 |
+
size 38853
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,374 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"glue": {
|
4 |
+
"acc,none": 0.5410165555026203,
|
5 |
+
"acc_stderr,none": 0.012289708247379585,
|
6 |
+
"f1,none": 0.3991229231036883,
|
7 |
+
"f1_stderr,none": 0.00018823773677900912,
|
8 |
+
"mcc,none": 0.028777377059353095,
|
9 |
+
"mcc_stderr,none": 0.029557452442007595,
|
10 |
+
"alias": "glue"
|
11 |
+
},
|
12 |
+
"cola": {
|
13 |
+
"mcc,none": 0.028777377059353095,
|
14 |
+
"mcc_stderr,none": 0.029557452442007595,
|
15 |
+
"alias": " - cola"
|
16 |
+
},
|
17 |
+
"mnli": {
|
18 |
+
"acc,none": 0.3502801833927662,
|
19 |
+
"acc_stderr,none": 0.004815571260570184,
|
20 |
+
"alias": " - mnli"
|
21 |
+
},
|
22 |
+
"mnli_mismatch": {
|
23 |
+
"acc,none": 0.3463181448331977,
|
24 |
+
"acc_stderr,none": 0.004798682211884212,
|
25 |
+
"alias": " - mnli_mismatch"
|
26 |
+
},
|
27 |
+
"mrpc": {
|
28 |
+
"acc,none": 0.37254901960784315,
|
29 |
+
"acc_stderr,none": 0.02396538492671658,
|
30 |
+
"f1,none": 0.26011560693641617,
|
31 |
+
"f1_stderr,none": 0.03106858780787724,
|
32 |
+
"alias": " - mrpc"
|
33 |
+
},
|
34 |
+
"qnli": {
|
35 |
+
"acc,none": 0.5052169137836353,
|
36 |
+
"acc_stderr,none": 0.006765042284363289,
|
37 |
+
"alias": " - qnli"
|
38 |
+
},
|
39 |
+
"qqp": {
|
40 |
+
"acc,none": 0.6368290873114024,
|
41 |
+
"acc_stderr,none": 0.002391775841486003,
|
42 |
+
"f1,none": 0.4003267306514192,
|
43 |
+
"f1_stderr,none": 0.003952746364902292,
|
44 |
+
"alias": " - qqp"
|
45 |
+
},
|
46 |
+
"rte": {
|
47 |
+
"acc,none": 0.51985559566787,
|
48 |
+
"acc_stderr,none": 0.030072723167317184,
|
49 |
+
"alias": " - rte"
|
50 |
+
},
|
51 |
+
"sst2": {
|
52 |
+
"acc,none": 0.7568807339449541,
|
53 |
+
"acc_stderr,none": 0.01453497656207427,
|
54 |
+
"alias": " - sst2"
|
55 |
+
},
|
56 |
+
"wnli": {
|
57 |
+
"acc,none": 0.4647887323943662,
|
58 |
+
"acc_stderr,none": 0.0596130578497224,
|
59 |
+
"alias": " - wnli"
|
60 |
+
}
|
61 |
+
},
|
62 |
+
"groups": {
|
63 |
+
"glue": {
|
64 |
+
"acc,none": 0.5410165555026203,
|
65 |
+
"acc_stderr,none": 0.012289708247379585,
|
66 |
+
"f1,none": 0.3991229231036883,
|
67 |
+
"f1_stderr,none": 0.00018823773677900912,
|
68 |
+
"mcc,none": 0.028777377059353095,
|
69 |
+
"mcc_stderr,none": 0.029557452442007595,
|
70 |
+
"alias": "glue"
|
71 |
+
}
|
72 |
+
},
|
73 |
+
"configs": {
|
74 |
+
"cola": {
|
75 |
+
"task": "cola",
|
76 |
+
"group": "glue",
|
77 |
+
"dataset_path": "glue",
|
78 |
+
"dataset_name": "cola",
|
79 |
+
"training_split": "train",
|
80 |
+
"validation_split": "validation",
|
81 |
+
"doc_to_text": "{{sentence}}\nQuestion: Does this sentence make sense?\nAnswer:",
|
82 |
+
"doc_to_target": "label",
|
83 |
+
"doc_to_choice": [
|
84 |
+
"no",
|
85 |
+
"yes"
|
86 |
+
],
|
87 |
+
"description": "",
|
88 |
+
"target_delimiter": " ",
|
89 |
+
"fewshot_delimiter": "\n\n",
|
90 |
+
"metric_list": [
|
91 |
+
{
|
92 |
+
"metric": "mcc"
|
93 |
+
}
|
94 |
+
],
|
95 |
+
"output_type": "multiple_choice",
|
96 |
+
"repeats": 1,
|
97 |
+
"should_decontaminate": true,
|
98 |
+
"doc_to_decontamination_query": "sentence",
|
99 |
+
"metadata": {
|
100 |
+
"version": 1.0
|
101 |
+
}
|
102 |
+
},
|
103 |
+
"mnli": {
|
104 |
+
"task": "mnli",
|
105 |
+
"group": "glue",
|
106 |
+
"dataset_path": "glue",
|
107 |
+
"dataset_name": "mnli",
|
108 |
+
"training_split": "train",
|
109 |
+
"validation_split": "validation_matched",
|
110 |
+
"doc_to_text": "def doc_to_text(doc) -> str:\n return \"{}\\nQuestion: {} True, False or Neither?\\nAnswer:\".format(\n doc[\"premise\"],\n doc[\"hypothesis\"].strip()\n + (\"\" if doc[\"hypothesis\"].strip().endswith(\".\") else \".\"),\n )\n",
|
111 |
+
"doc_to_target": "label",
|
112 |
+
"doc_to_choice": [
|
113 |
+
"True",
|
114 |
+
"Neither",
|
115 |
+
"False"
|
116 |
+
],
|
117 |
+
"description": "",
|
118 |
+
"target_delimiter": " ",
|
119 |
+
"fewshot_delimiter": "\n\n",
|
120 |
+
"metric_list": [
|
121 |
+
{
|
122 |
+
"metric": "acc"
|
123 |
+
}
|
124 |
+
],
|
125 |
+
"output_type": "multiple_choice",
|
126 |
+
"repeats": 1,
|
127 |
+
"should_decontaminate": false,
|
128 |
+
"metadata": {
|
129 |
+
"version": 1.0
|
130 |
+
}
|
131 |
+
},
|
132 |
+
"mnli_mismatch": {
|
133 |
+
"task": "mnli_mismatch",
|
134 |
+
"group": "glue",
|
135 |
+
"dataset_path": "glue",
|
136 |
+
"dataset_name": "mnli",
|
137 |
+
"training_split": "train",
|
138 |
+
"validation_split": "validation_mismatched",
|
139 |
+
"doc_to_text": "def doc_to_text(doc) -> str:\n return \"{}\\nQuestion: {} True, False or Neither?\\nAnswer:\".format(\n doc[\"premise\"],\n doc[\"hypothesis\"].strip()\n + (\"\" if doc[\"hypothesis\"].strip().endswith(\".\") else \".\"),\n )\n",
|
140 |
+
"doc_to_target": "label",
|
141 |
+
"doc_to_choice": [
|
142 |
+
"True",
|
143 |
+
"Neither",
|
144 |
+
"False"
|
145 |
+
],
|
146 |
+
"description": "",
|
147 |
+
"target_delimiter": " ",
|
148 |
+
"fewshot_delimiter": "\n\n",
|
149 |
+
"metric_list": [
|
150 |
+
{
|
151 |
+
"metric": "acc"
|
152 |
+
}
|
153 |
+
],
|
154 |
+
"output_type": "multiple_choice",
|
155 |
+
"repeats": 1,
|
156 |
+
"should_decontaminate": false,
|
157 |
+
"metadata": {
|
158 |
+
"version": 1.0
|
159 |
+
}
|
160 |
+
},
|
161 |
+
"mrpc": {
|
162 |
+
"task": "mrpc",
|
163 |
+
"group": "glue",
|
164 |
+
"dataset_path": "glue",
|
165 |
+
"dataset_name": "mrpc",
|
166 |
+
"training_split": "train",
|
167 |
+
"validation_split": "validation",
|
168 |
+
"doc_to_text": "Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Do both sentences mean the same thing?\nAnswer:",
|
169 |
+
"doc_to_target": "label",
|
170 |
+
"doc_to_choice": [
|
171 |
+
"no",
|
172 |
+
"yes"
|
173 |
+
],
|
174 |
+
"description": "",
|
175 |
+
"target_delimiter": " ",
|
176 |
+
"fewshot_delimiter": "\n\n",
|
177 |
+
"metric_list": [
|
178 |
+
{
|
179 |
+
"metric": "acc"
|
180 |
+
},
|
181 |
+
{
|
182 |
+
"metric": "f1"
|
183 |
+
}
|
184 |
+
],
|
185 |
+
"output_type": "multiple_choice",
|
186 |
+
"repeats": 1,
|
187 |
+
"should_decontaminate": false,
|
188 |
+
"metadata": {
|
189 |
+
"version": 1.0
|
190 |
+
}
|
191 |
+
},
|
192 |
+
"qnli": {
|
193 |
+
"task": "qnli",
|
194 |
+
"group": "glue",
|
195 |
+
"dataset_path": "glue",
|
196 |
+
"dataset_name": "qnli",
|
197 |
+
"training_split": "train",
|
198 |
+
"validation_split": "validation",
|
199 |
+
"doc_to_text": "{{question}}\n{{sentence}}\nQuestion: Does this response answer the question?\nAnswer:",
|
200 |
+
"doc_to_target": "label",
|
201 |
+
"doc_to_choice": [
|
202 |
+
"yes",
|
203 |
+
"no"
|
204 |
+
],
|
205 |
+
"description": "",
|
206 |
+
"target_delimiter": " ",
|
207 |
+
"fewshot_delimiter": "\n\n",
|
208 |
+
"metric_list": [
|
209 |
+
{
|
210 |
+
"metric": "acc"
|
211 |
+
}
|
212 |
+
],
|
213 |
+
"output_type": "multiple_choice",
|
214 |
+
"repeats": 1,
|
215 |
+
"should_decontaminate": false,
|
216 |
+
"metadata": {
|
217 |
+
"version": 1.0
|
218 |
+
}
|
219 |
+
},
|
220 |
+
"qqp": {
|
221 |
+
"task": "qqp",
|
222 |
+
"group": "glue",
|
223 |
+
"dataset_path": "glue",
|
224 |
+
"dataset_name": "qqp",
|
225 |
+
"training_split": "train",
|
226 |
+
"validation_split": "validation",
|
227 |
+
"doc_to_text": "\nSentence 1: {{question1}}\nSentence 2: {{question2}}\nAnswer:",
|
228 |
+
"doc_to_target": "label",
|
229 |
+
"doc_to_choice": [
|
230 |
+
"no",
|
231 |
+
"yes"
|
232 |
+
],
|
233 |
+
"description": "",
|
234 |
+
"target_delimiter": " ",
|
235 |
+
"fewshot_delimiter": "\n\n",
|
236 |
+
"metric_list": [
|
237 |
+
{
|
238 |
+
"metric": "acc"
|
239 |
+
},
|
240 |
+
{
|
241 |
+
"metric": "f1"
|
242 |
+
}
|
243 |
+
],
|
244 |
+
"output_type": "multiple_choice",
|
245 |
+
"repeats": 1,
|
246 |
+
"should_decontaminate": false,
|
247 |
+
"metadata": {
|
248 |
+
"version": 1.0
|
249 |
+
}
|
250 |
+
},
|
251 |
+
"rte": {
|
252 |
+
"task": "rte",
|
253 |
+
"group": "glue",
|
254 |
+
"dataset_path": "glue",
|
255 |
+
"dataset_name": "rte",
|
256 |
+
"training_split": "train",
|
257 |
+
"validation_split": "validation",
|
258 |
+
"doc_to_text": "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:",
|
259 |
+
"doc_to_target": "label",
|
260 |
+
"doc_to_choice": [
|
261 |
+
"True",
|
262 |
+
"False"
|
263 |
+
],
|
264 |
+
"description": "",
|
265 |
+
"target_delimiter": " ",
|
266 |
+
"fewshot_delimiter": "\n\n",
|
267 |
+
"metric_list": [
|
268 |
+
{
|
269 |
+
"metric": "acc"
|
270 |
+
}
|
271 |
+
],
|
272 |
+
"output_type": "multiple_choice",
|
273 |
+
"repeats": 1,
|
274 |
+
"should_decontaminate": false,
|
275 |
+
"metadata": {
|
276 |
+
"version": 1.0
|
277 |
+
}
|
278 |
+
},
|
279 |
+
"sst2": {
|
280 |
+
"task": "sst2",
|
281 |
+
"group": "glue",
|
282 |
+
"dataset_path": "glue",
|
283 |
+
"dataset_name": "sst2",
|
284 |
+
"training_split": "train",
|
285 |
+
"validation_split": "validation",
|
286 |
+
"doc_to_text": "{{sentence}}\nQuestion: Is this sentence positive or negative?\nAnswer:",
|
287 |
+
"doc_to_target": "label",
|
288 |
+
"doc_to_choice": [
|
289 |
+
"negative",
|
290 |
+
"positive"
|
291 |
+
],
|
292 |
+
"description": "",
|
293 |
+
"target_delimiter": " ",
|
294 |
+
"fewshot_delimiter": "\n\n",
|
295 |
+
"metric_list": [
|
296 |
+
{
|
297 |
+
"metric": "acc"
|
298 |
+
}
|
299 |
+
],
|
300 |
+
"output_type": "multiple_choice",
|
301 |
+
"repeats": 1,
|
302 |
+
"should_decontaminate": false,
|
303 |
+
"metadata": {
|
304 |
+
"version": 1.0
|
305 |
+
}
|
306 |
+
},
|
307 |
+
"wnli": {
|
308 |
+
"task": "wnli",
|
309 |
+
"group": "glue",
|
310 |
+
"dataset_path": "glue",
|
311 |
+
"dataset_name": "wnli",
|
312 |
+
"training_split": "train",
|
313 |
+
"validation_split": "validation",
|
314 |
+
"doc_to_text": "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:",
|
315 |
+
"doc_to_target": "label",
|
316 |
+
"doc_to_choice": [
|
317 |
+
"False",
|
318 |
+
"True"
|
319 |
+
],
|
320 |
+
"description": "",
|
321 |
+
"target_delimiter": " ",
|
322 |
+
"fewshot_delimiter": "\n\n",
|
323 |
+
"metric_list": [
|
324 |
+
{
|
325 |
+
"metric": "acc"
|
326 |
+
}
|
327 |
+
],
|
328 |
+
"output_type": "multiple_choice",
|
329 |
+
"repeats": 1,
|
330 |
+
"should_decontaminate": false,
|
331 |
+
"metadata": {
|
332 |
+
"version": 2.0
|
333 |
+
}
|
334 |
+
}
|
335 |
+
},
|
336 |
+
"versions": {
|
337 |
+
"cola": 1.0,
|
338 |
+
"glue": "N/A",
|
339 |
+
"mnli": 1.0,
|
340 |
+
"mnli_mismatch": 1.0,
|
341 |
+
"mrpc": 1.0,
|
342 |
+
"qnli": 1.0,
|
343 |
+
"qqp": 1.0,
|
344 |
+
"rte": 1.0,
|
345 |
+
"sst2": 1.0,
|
346 |
+
"wnli": 2.0
|
347 |
+
},
|
348 |
+
"n-shot": {
|
349 |
+
"cola": 0,
|
350 |
+
"glue": 0,
|
351 |
+
"mnli": 0,
|
352 |
+
"mnli_mismatch": 0,
|
353 |
+
"mrpc": 0,
|
354 |
+
"qnli": 0,
|
355 |
+
"qqp": 0,
|
356 |
+
"rte": 0,
|
357 |
+
"sst2": 0,
|
358 |
+
"wnli": 0
|
359 |
+
},
|
360 |
+
"config": {
|
361 |
+
"model": "hf",
|
362 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
363 |
+
"batch_size": "auto",
|
364 |
+
"batch_sizes": [
|
365 |
+
64
|
366 |
+
],
|
367 |
+
"device": null,
|
368 |
+
"use_cache": null,
|
369 |
+
"limit": null,
|
370 |
+
"bootstrap_iters": 100000,
|
371 |
+
"gen_kwargs": null
|
372 |
+
},
|
373 |
+
"git_hash": "1ee41f7"
|
374 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:97e96b14c2cb3ac4e0abc67fb944f31147a9fa20fb2aedaaeac5db3f0a20df4c
|
3 |
+
size 102917
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,67 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"hellaswag": {
|
4 |
+
"acc,none": 0.42471619199362676,
|
5 |
+
"acc_stderr,none": 0.004932896472460568,
|
6 |
+
"acc_norm,none": 0.5501892053375822,
|
7 |
+
"acc_norm_stderr,none": 0.004964579685712438,
|
8 |
+
"alias": "hellaswag"
|
9 |
+
}
|
10 |
+
},
|
11 |
+
"configs": {
|
12 |
+
"hellaswag": {
|
13 |
+
"task": "hellaswag",
|
14 |
+
"group": [
|
15 |
+
"multiple_choice"
|
16 |
+
],
|
17 |
+
"dataset_path": "hellaswag",
|
18 |
+
"training_split": "train",
|
19 |
+
"validation_split": "validation",
|
20 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
21 |
+
"doc_to_text": "{{query}}",
|
22 |
+
"doc_to_target": "{{label}}",
|
23 |
+
"doc_to_choice": "choices",
|
24 |
+
"description": "",
|
25 |
+
"target_delimiter": " ",
|
26 |
+
"fewshot_delimiter": "\n\n",
|
27 |
+
"metric_list": [
|
28 |
+
{
|
29 |
+
"metric": "acc",
|
30 |
+
"aggregation": "mean",
|
31 |
+
"higher_is_better": true
|
32 |
+
},
|
33 |
+
{
|
34 |
+
"metric": "acc_norm",
|
35 |
+
"aggregation": "mean",
|
36 |
+
"higher_is_better": true
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"output_type": "multiple_choice",
|
40 |
+
"repeats": 1,
|
41 |
+
"should_decontaminate": false,
|
42 |
+
"metadata": {
|
43 |
+
"version": 1.0
|
44 |
+
}
|
45 |
+
}
|
46 |
+
},
|
47 |
+
"versions": {
|
48 |
+
"hellaswag": 1.0
|
49 |
+
},
|
50 |
+
"n-shot": {
|
51 |
+
"hellaswag": 0
|
52 |
+
},
|
53 |
+
"config": {
|
54 |
+
"model": "hf",
|
55 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
56 |
+
"batch_size": "auto",
|
57 |
+
"batch_sizes": [
|
58 |
+
64
|
59 |
+
],
|
60 |
+
"device": null,
|
61 |
+
"use_cache": null,
|
62 |
+
"limit": null,
|
63 |
+
"bootstrap_iters": 100000,
|
64 |
+
"gen_kwargs": null
|
65 |
+
},
|
66 |
+
"git_hash": "1ee41f7"
|
67 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2a306a64419e9c542fa6abc1781910a4d4b282ddd0c1f6093aeb2e1c2b274b92
|
3 |
+
size 81828
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,126 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"lambada": {
|
4 |
+
"perplexity,none": 6.369187608169782,
|
5 |
+
"perplexity_stderr,none": 0.6794074695255675,
|
6 |
+
"acc,none": 0.6095478362119154,
|
7 |
+
"acc_stderr,none": 0.02462399103409058,
|
8 |
+
"alias": "lambada"
|
9 |
+
},
|
10 |
+
"lambada_openai": {
|
11 |
+
"perplexity,none": 5.0536798166390575,
|
12 |
+
"perplexity_stderr,none": 0.11842491248398582,
|
13 |
+
"acc,none": 0.6568988938482437,
|
14 |
+
"acc_stderr,none": 0.006614124982461028,
|
15 |
+
"alias": " - lambada_openai"
|
16 |
+
},
|
17 |
+
"lambada_standard": {
|
18 |
+
"perplexity,none": 7.684695399700504,
|
19 |
+
"perplexity_stderr,none": 0.20929842195468237,
|
20 |
+
"acc,none": 0.562196778575587,
|
21 |
+
"acc_stderr,none": 0.006911872616149982,
|
22 |
+
"alias": " - lambada_standard"
|
23 |
+
}
|
24 |
+
},
|
25 |
+
"groups": {
|
26 |
+
"lambada": {
|
27 |
+
"perplexity,none": 6.369187608169782,
|
28 |
+
"perplexity_stderr,none": 0.6794074695255675,
|
29 |
+
"acc,none": 0.6095478362119154,
|
30 |
+
"acc_stderr,none": 0.02462399103409058,
|
31 |
+
"alias": "lambada"
|
32 |
+
}
|
33 |
+
},
|
34 |
+
"configs": {
|
35 |
+
"lambada_openai": {
|
36 |
+
"task": "lambada_openai",
|
37 |
+
"group": [
|
38 |
+
"lambada"
|
39 |
+
],
|
40 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
41 |
+
"dataset_name": "default",
|
42 |
+
"test_split": "test",
|
43 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
44 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
45 |
+
"description": "",
|
46 |
+
"target_delimiter": " ",
|
47 |
+
"fewshot_delimiter": "\n\n",
|
48 |
+
"metric_list": [
|
49 |
+
{
|
50 |
+
"metric": "perplexity",
|
51 |
+
"aggregation": "perplexity",
|
52 |
+
"higher_is_better": false
|
53 |
+
},
|
54 |
+
{
|
55 |
+
"metric": "acc",
|
56 |
+
"aggregation": "mean",
|
57 |
+
"higher_is_better": true
|
58 |
+
}
|
59 |
+
],
|
60 |
+
"output_type": "loglikelihood",
|
61 |
+
"repeats": 1,
|
62 |
+
"should_decontaminate": true,
|
63 |
+
"doc_to_decontamination_query": "{{text}}",
|
64 |
+
"metadata": {
|
65 |
+
"version": 1.0
|
66 |
+
}
|
67 |
+
},
|
68 |
+
"lambada_standard": {
|
69 |
+
"task": "lambada_standard",
|
70 |
+
"group": [
|
71 |
+
"lambada"
|
72 |
+
],
|
73 |
+
"dataset_path": "lambada",
|
74 |
+
"validation_split": "validation",
|
75 |
+
"test_split": "test",
|
76 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
77 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
78 |
+
"description": "",
|
79 |
+
"target_delimiter": " ",
|
80 |
+
"fewshot_delimiter": "\n\n",
|
81 |
+
"metric_list": [
|
82 |
+
{
|
83 |
+
"metric": "perplexity",
|
84 |
+
"aggregation": "perplexity",
|
85 |
+
"higher_is_better": false
|
86 |
+
},
|
87 |
+
{
|
88 |
+
"metric": "acc",
|
89 |
+
"aggregation": "mean",
|
90 |
+
"higher_is_better": true
|
91 |
+
}
|
92 |
+
],
|
93 |
+
"output_type": "loglikelihood",
|
94 |
+
"repeats": 1,
|
95 |
+
"should_decontaminate": true,
|
96 |
+
"doc_to_decontamination_query": "{{text}}",
|
97 |
+
"metadata": {
|
98 |
+
"version": 1.0
|
99 |
+
}
|
100 |
+
}
|
101 |
+
},
|
102 |
+
"versions": {
|
103 |
+
"lambada": "N/A",
|
104 |
+
"lambada_openai": 1.0,
|
105 |
+
"lambada_standard": 1.0
|
106 |
+
},
|
107 |
+
"n-shot": {
|
108 |
+
"lambada": 0,
|
109 |
+
"lambada_openai": 0,
|
110 |
+
"lambada_standard": 0
|
111 |
+
},
|
112 |
+
"config": {
|
113 |
+
"model": "hf",
|
114 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
115 |
+
"batch_size": "auto",
|
116 |
+
"batch_sizes": [
|
117 |
+
64
|
118 |
+
],
|
119 |
+
"device": null,
|
120 |
+
"use_cache": null,
|
121 |
+
"limit": null,
|
122 |
+
"bootstrap_iters": 100000,
|
123 |
+
"gen_kwargs": null
|
124 |
+
},
|
125 |
+
"git_hash": "1ee41f7"
|
126 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6a324eedb84b2a9b3e9d538e30db7e1aaae7e3e66d68e7de29d28627092c3b10
|
3 |
+
size 48681
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,252 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"lambada_multilingual": {
|
4 |
+
"perplexity,none": 43.18680498264333,
|
5 |
+
"perplexity_stderr,none": 16.58118499444968,
|
6 |
+
"acc,none": 0.4484766155637493,
|
7 |
+
"acc_stderr,none": 0.0830249431644644,
|
8 |
+
"alias": "lambada_multilingual"
|
9 |
+
},
|
10 |
+
"lambada_openai_mt_de": {
|
11 |
+
"perplexity,none": 65.82972989107675,
|
12 |
+
"perplexity_stderr,none": 3.9571956126281833,
|
13 |
+
"acc,none": 0.35066951290510384,
|
14 |
+
"acc_stderr,none": 0.006648045374603887,
|
15 |
+
"alias": " - lambada_openai_mt_de"
|
16 |
+
},
|
17 |
+
"lambada_openai_mt_en": {
|
18 |
+
"perplexity,none": 5.056405351554518,
|
19 |
+
"perplexity_stderr,none": 0.11860916891457675,
|
20 |
+
"acc,none": 0.6567048321366195,
|
21 |
+
"acc_stderr,none": 0.00661501790443367,
|
22 |
+
"alias": " - lambada_openai_mt_en"
|
23 |
+
},
|
24 |
+
"lambada_openai_mt_es": {
|
25 |
+
"perplexity,none": 61.249035187327245,
|
26 |
+
"perplexity_stderr,none": 3.3251943349532094,
|
27 |
+
"acc,none": 0.37104599262565496,
|
28 |
+
"acc_stderr,none": 0.006730314981342215,
|
29 |
+
"alias": " - lambada_openai_mt_es"
|
30 |
+
},
|
31 |
+
"lambada_openai_mt_fr": {
|
32 |
+
"perplexity,none": 34.89400012412681,
|
33 |
+
"perplexity_stderr,none": 1.8764986780815518,
|
34 |
+
"acc,none": 0.44944692412187076,
|
35 |
+
"acc_stderr,none": 0.006930281504471643,
|
36 |
+
"alias": " - lambada_openai_mt_fr"
|
37 |
+
},
|
38 |
+
"lambada_openai_mt_it": {
|
39 |
+
"perplexity,none": 48.90485435913133,
|
40 |
+
"perplexity_stderr,none": 2.8348284694345787,
|
41 |
+
"acc,none": 0.4145158160294974,
|
42 |
+
"acc_stderr,none": 0.006863414211397148,
|
43 |
+
"alias": " - lambada_openai_mt_it"
|
44 |
+
}
|
45 |
+
},
|
46 |
+
"groups": {
|
47 |
+
"lambada_multilingual": {
|
48 |
+
"perplexity,none": 43.18680498264333,
|
49 |
+
"perplexity_stderr,none": 16.58118499444968,
|
50 |
+
"acc,none": 0.4484766155637493,
|
51 |
+
"acc_stderr,none": 0.0830249431644644,
|
52 |
+
"alias": "lambada_multilingual"
|
53 |
+
}
|
54 |
+
},
|
55 |
+
"configs": {
|
56 |
+
"lambada_openai_mt_de": {
|
57 |
+
"task": "lambada_openai_mt_de",
|
58 |
+
"group": [
|
59 |
+
"lambada_multilingual"
|
60 |
+
],
|
61 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
62 |
+
"dataset_name": "de",
|
63 |
+
"test_split": "test",
|
64 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
65 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
66 |
+
"description": "",
|
67 |
+
"target_delimiter": " ",
|
68 |
+
"fewshot_delimiter": "\n\n",
|
69 |
+
"metric_list": [
|
70 |
+
{
|
71 |
+
"metric": "perplexity",
|
72 |
+
"aggregation": "perplexity",
|
73 |
+
"higher_is_better": false
|
74 |
+
},
|
75 |
+
{
|
76 |
+
"metric": "acc",
|
77 |
+
"aggregation": "mean",
|
78 |
+
"higher_is_better": true
|
79 |
+
}
|
80 |
+
],
|
81 |
+
"output_type": "loglikelihood",
|
82 |
+
"repeats": 1,
|
83 |
+
"should_decontaminate": true,
|
84 |
+
"doc_to_decontamination_query": "{{text}}",
|
85 |
+
"metadata": {
|
86 |
+
"version": 1.0
|
87 |
+
}
|
88 |
+
},
|
89 |
+
"lambada_openai_mt_en": {
|
90 |
+
"task": "lambada_openai_mt_en",
|
91 |
+
"group": [
|
92 |
+
"lambada_multilingual"
|
93 |
+
],
|
94 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
95 |
+
"dataset_name": "en",
|
96 |
+
"test_split": "test",
|
97 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
98 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
99 |
+
"description": "",
|
100 |
+
"target_delimiter": " ",
|
101 |
+
"fewshot_delimiter": "\n\n",
|
102 |
+
"metric_list": [
|
103 |
+
{
|
104 |
+
"metric": "perplexity",
|
105 |
+
"aggregation": "perplexity",
|
106 |
+
"higher_is_better": false
|
107 |
+
},
|
108 |
+
{
|
109 |
+
"metric": "acc",
|
110 |
+
"aggregation": "mean",
|
111 |
+
"higher_is_better": true
|
112 |
+
}
|
113 |
+
],
|
114 |
+
"output_type": "loglikelihood",
|
115 |
+
"repeats": 1,
|
116 |
+
"should_decontaminate": true,
|
117 |
+
"doc_to_decontamination_query": "{{text}}",
|
118 |
+
"metadata": {
|
119 |
+
"version": 1.0
|
120 |
+
}
|
121 |
+
},
|
122 |
+
"lambada_openai_mt_es": {
|
123 |
+
"task": "lambada_openai_mt_es",
|
124 |
+
"group": [
|
125 |
+
"lambada_multilingual"
|
126 |
+
],
|
127 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
128 |
+
"dataset_name": "es",
|
129 |
+
"test_split": "test",
|
130 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
131 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
132 |
+
"description": "",
|
133 |
+
"target_delimiter": " ",
|
134 |
+
"fewshot_delimiter": "\n\n",
|
135 |
+
"metric_list": [
|
136 |
+
{
|
137 |
+
"metric": "perplexity",
|
138 |
+
"aggregation": "perplexity",
|
139 |
+
"higher_is_better": false
|
140 |
+
},
|
141 |
+
{
|
142 |
+
"metric": "acc",
|
143 |
+
"aggregation": "mean",
|
144 |
+
"higher_is_better": true
|
145 |
+
}
|
146 |
+
],
|
147 |
+
"output_type": "loglikelihood",
|
148 |
+
"repeats": 1,
|
149 |
+
"should_decontaminate": true,
|
150 |
+
"doc_to_decontamination_query": "{{text}}",
|
151 |
+
"metadata": {
|
152 |
+
"version": 1.0
|
153 |
+
}
|
154 |
+
},
|
155 |
+
"lambada_openai_mt_fr": {
|
156 |
+
"task": "lambada_openai_mt_fr",
|
157 |
+
"group": [
|
158 |
+
"lambada_multilingual"
|
159 |
+
],
|
160 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
161 |
+
"dataset_name": "fr",
|
162 |
+
"test_split": "test",
|
163 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
164 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
165 |
+
"description": "",
|
166 |
+
"target_delimiter": " ",
|
167 |
+
"fewshot_delimiter": "\n\n",
|
168 |
+
"metric_list": [
|
169 |
+
{
|
170 |
+
"metric": "perplexity",
|
171 |
+
"aggregation": "perplexity",
|
172 |
+
"higher_is_better": false
|
173 |
+
},
|
174 |
+
{
|
175 |
+
"metric": "acc",
|
176 |
+
"aggregation": "mean",
|
177 |
+
"higher_is_better": true
|
178 |
+
}
|
179 |
+
],
|
180 |
+
"output_type": "loglikelihood",
|
181 |
+
"repeats": 1,
|
182 |
+
"should_decontaminate": true,
|
183 |
+
"doc_to_decontamination_query": "{{text}}",
|
184 |
+
"metadata": {
|
185 |
+
"version": 1.0
|
186 |
+
}
|
187 |
+
},
|
188 |
+
"lambada_openai_mt_it": {
|
189 |
+
"task": "lambada_openai_mt_it",
|
190 |
+
"group": [
|
191 |
+
"lambada_multilingual"
|
192 |
+
],
|
193 |
+
"dataset_path": "EleutherAI/lambada_openai",
|
194 |
+
"dataset_name": "it",
|
195 |
+
"test_split": "test",
|
196 |
+
"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
197 |
+
"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
198 |
+
"description": "",
|
199 |
+
"target_delimiter": " ",
|
200 |
+
"fewshot_delimiter": "\n\n",
|
201 |
+
"metric_list": [
|
202 |
+
{
|
203 |
+
"metric": "perplexity",
|
204 |
+
"aggregation": "perplexity",
|
205 |
+
"higher_is_better": false
|
206 |
+
},
|
207 |
+
{
|
208 |
+
"metric": "acc",
|
209 |
+
"aggregation": "mean",
|
210 |
+
"higher_is_better": true
|
211 |
+
}
|
212 |
+
],
|
213 |
+
"output_type": "loglikelihood",
|
214 |
+
"repeats": 1,
|
215 |
+
"should_decontaminate": true,
|
216 |
+
"doc_to_decontamination_query": "{{text}}",
|
217 |
+
"metadata": {
|
218 |
+
"version": 1.0
|
219 |
+
}
|
220 |
+
}
|
221 |
+
},
|
222 |
+
"versions": {
|
223 |
+
"lambada_multilingual": "N/A",
|
224 |
+
"lambada_openai_mt_de": 1.0,
|
225 |
+
"lambada_openai_mt_en": 1.0,
|
226 |
+
"lambada_openai_mt_es": 1.0,
|
227 |
+
"lambada_openai_mt_fr": 1.0,
|
228 |
+
"lambada_openai_mt_it": 1.0
|
229 |
+
},
|
230 |
+
"n-shot": {
|
231 |
+
"lambada_multilingual": 0,
|
232 |
+
"lambada_openai_mt_de": 0,
|
233 |
+
"lambada_openai_mt_en": 0,
|
234 |
+
"lambada_openai_mt_es": 0,
|
235 |
+
"lambada_openai_mt_fr": 0,
|
236 |
+
"lambada_openai_mt_it": 0
|
237 |
+
},
|
238 |
+
"config": {
|
239 |
+
"model": "hf",
|
240 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
241 |
+
"batch_size": "auto",
|
242 |
+
"batch_sizes": [
|
243 |
+
64
|
244 |
+
],
|
245 |
+
"device": null,
|
246 |
+
"use_cache": null,
|
247 |
+
"limit": null,
|
248 |
+
"bootstrap_iters": 100000,
|
249 |
+
"gen_kwargs": null
|
250 |
+
},
|
251 |
+
"git_hash": "1ee41f7"
|
252 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:80c887527c7dfb35056731a2e6af994e317fbb91a7f06ef2646439d1d88fe944
|
3 |
+
size 60619
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,2594 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"mmlu": {
|
4 |
+
"acc,none": 0.2525993448226748,
|
5 |
+
"acc_stderr,none": 0.04202282990456397,
|
6 |
+
"alias": "mmlu"
|
7 |
+
},
|
8 |
+
"mmlu_humanities": {
|
9 |
+
"alias": " - humanities",
|
10 |
+
"acc,none": 0.24017003188097769,
|
11 |
+
"acc_stderr,none": 0.02857393482131495
|
12 |
+
},
|
13 |
+
"mmlu_formal_logic": {
|
14 |
+
"alias": " - formal_logic",
|
15 |
+
"acc,none": 0.2857142857142857,
|
16 |
+
"acc_stderr,none": 0.040406101782088394
|
17 |
+
},
|
18 |
+
"mmlu_high_school_european_history": {
|
19 |
+
"alias": " - high_school_european_history",
|
20 |
+
"acc,none": 0.23030303030303031,
|
21 |
+
"acc_stderr,none": 0.03287666758603489
|
22 |
+
},
|
23 |
+
"mmlu_high_school_us_history": {
|
24 |
+
"alias": " - high_school_us_history",
|
25 |
+
"acc,none": 0.27941176470588236,
|
26 |
+
"acc_stderr,none": 0.031493281045079556
|
27 |
+
},
|
28 |
+
"mmlu_high_school_world_history": {
|
29 |
+
"alias": " - high_school_world_history",
|
30 |
+
"acc,none": 0.2489451476793249,
|
31 |
+
"acc_stderr,none": 0.028146970599422644
|
32 |
+
},
|
33 |
+
"mmlu_international_law": {
|
34 |
+
"alias": " - international_law",
|
35 |
+
"acc,none": 0.17355371900826447,
|
36 |
+
"acc_stderr,none": 0.0345727283691767
|
37 |
+
},
|
38 |
+
"mmlu_jurisprudence": {
|
39 |
+
"alias": " - jurisprudence",
|
40 |
+
"acc,none": 0.25,
|
41 |
+
"acc_stderr,none": 0.04186091791394607
|
42 |
+
},
|
43 |
+
"mmlu_logical_fallacies": {
|
44 |
+
"alias": " - logical_fallacies",
|
45 |
+
"acc,none": 0.25153374233128833,
|
46 |
+
"acc_stderr,none": 0.034089978868575295
|
47 |
+
},
|
48 |
+
"mmlu_moral_disputes": {
|
49 |
+
"alias": " - moral_disputes",
|
50 |
+
"acc,none": 0.21098265895953758,
|
51 |
+
"acc_stderr,none": 0.021966309947043124
|
52 |
+
},
|
53 |
+
"mmlu_moral_scenarios": {
|
54 |
+
"alias": " - moral_scenarios",
|
55 |
+
"acc,none": 0.2346368715083799,
|
56 |
+
"acc_stderr,none": 0.014173044098303679
|
57 |
+
},
|
58 |
+
"mmlu_philosophy": {
|
59 |
+
"alias": " - philosophy",
|
60 |
+
"acc,none": 0.2540192926045016,
|
61 |
+
"acc_stderr,none": 0.02472386150477169
|
62 |
+
},
|
63 |
+
"mmlu_prehistory": {
|
64 |
+
"alias": " - prehistory",
|
65 |
+
"acc,none": 0.2222222222222222,
|
66 |
+
"acc_stderr,none": 0.023132376234543346
|
67 |
+
},
|
68 |
+
"mmlu_professional_law": {
|
69 |
+
"alias": " - professional_law",
|
70 |
+
"acc,none": 0.24967405475880053,
|
71 |
+
"acc_stderr,none": 0.011054538377832327
|
72 |
+
},
|
73 |
+
"mmlu_world_religions": {
|
74 |
+
"alias": " - world_religions",
|
75 |
+
"acc,none": 0.19883040935672514,
|
76 |
+
"acc_stderr,none": 0.03061111655743253
|
77 |
+
},
|
78 |
+
"mmlu_other": {
|
79 |
+
"alias": " - other",
|
80 |
+
"acc,none": 0.25683939491470875,
|
81 |
+
"acc_stderr,none": 0.05743915320464653
|
82 |
+
},
|
83 |
+
"mmlu_business_ethics": {
|
84 |
+
"alias": " - business_ethics",
|
85 |
+
"acc,none": 0.34,
|
86 |
+
"acc_stderr,none": 0.04760952285695235
|
87 |
+
},
|
88 |
+
"mmlu_clinical_knowledge": {
|
89 |
+
"alias": " - clinical_knowledge",
|
90 |
+
"acc,none": 0.32075471698113206,
|
91 |
+
"acc_stderr,none": 0.028727502957880263
|
92 |
+
},
|
93 |
+
"mmlu_college_medicine": {
|
94 |
+
"alias": " - college_medicine",
|
95 |
+
"acc,none": 0.3236994219653179,
|
96 |
+
"acc_stderr,none": 0.03567603799639171
|
97 |
+
},
|
98 |
+
"mmlu_global_facts": {
|
99 |
+
"alias": " - global_facts",
|
100 |
+
"acc,none": 0.2,
|
101 |
+
"acc_stderr,none": 0.04020151261036845
|
102 |
+
},
|
103 |
+
"mmlu_human_aging": {
|
104 |
+
"alias": " - human_aging",
|
105 |
+
"acc,none": 0.16143497757847533,
|
106 |
+
"acc_stderr,none": 0.024693957899128472
|
107 |
+
},
|
108 |
+
"mmlu_management": {
|
109 |
+
"alias": " - management",
|
110 |
+
"acc,none": 0.39805825242718446,
|
111 |
+
"acc_stderr,none": 0.04846748253977239
|
112 |
+
},
|
113 |
+
"mmlu_marketing": {
|
114 |
+
"alias": " - marketing",
|
115 |
+
"acc,none": 0.2094017094017094,
|
116 |
+
"acc_stderr,none": 0.026655699653922754
|
117 |
+
},
|
118 |
+
"mmlu_medical_genetics": {
|
119 |
+
"alias": " - medical_genetics",
|
120 |
+
"acc,none": 0.32,
|
121 |
+
"acc_stderr,none": 0.04688261722621505
|
122 |
+
},
|
123 |
+
"mmlu_miscellaneous": {
|
124 |
+
"alias": " - miscellaneous",
|
125 |
+
"acc,none": 0.22349936143039592,
|
126 |
+
"acc_stderr,none": 0.014897235229450707
|
127 |
+
},
|
128 |
+
"mmlu_nutrition": {
|
129 |
+
"alias": " - nutrition",
|
130 |
+
"acc,none": 0.30718954248366015,
|
131 |
+
"acc_stderr,none": 0.026415601914388992
|
132 |
+
},
|
133 |
+
"mmlu_professional_accounting": {
|
134 |
+
"alias": " - professional_accounting",
|
135 |
+
"acc,none": 0.24822695035460993,
|
136 |
+
"acc_stderr,none": 0.025770015644290396
|
137 |
+
},
|
138 |
+
"mmlu_professional_medicine": {
|
139 |
+
"alias": " - professional_medicine",
|
140 |
+
"acc,none": 0.25735294117647056,
|
141 |
+
"acc_stderr,none": 0.026556519470041524
|
142 |
+
},
|
143 |
+
"mmlu_virology": {
|
144 |
+
"alias": " - virology",
|
145 |
+
"acc,none": 0.21686746987951808,
|
146 |
+
"acc_stderr,none": 0.03208284450356365
|
147 |
+
},
|
148 |
+
"mmlu_social_sciences": {
|
149 |
+
"alias": " - social_sciences",
|
150 |
+
"acc,none": 0.26454338641533964,
|
151 |
+
"acc_stderr,none": 0.034586953407146494
|
152 |
+
},
|
153 |
+
"mmlu_econometrics": {
|
154 |
+
"alias": " - econometrics",
|
155 |
+
"acc,none": 0.2719298245614035,
|
156 |
+
"acc_stderr,none": 0.04185774424022056
|
157 |
+
},
|
158 |
+
"mmlu_high_school_geography": {
|
159 |
+
"alias": " - high_school_geography",
|
160 |
+
"acc,none": 0.3333333333333333,
|
161 |
+
"acc_stderr,none": 0.03358618145732524
|
162 |
+
},
|
163 |
+
"mmlu_high_school_government_and_politics": {
|
164 |
+
"alias": " - high_school_government_and_politics",
|
165 |
+
"acc,none": 0.27461139896373055,
|
166 |
+
"acc_stderr,none": 0.032210245080411544
|
167 |
+
},
|
168 |
+
"mmlu_high_school_macroeconomics": {
|
169 |
+
"alias": " - high_school_macroeconomics",
|
170 |
+
"acc,none": 0.258974358974359,
|
171 |
+
"acc_stderr,none": 0.022211106810061665
|
172 |
+
},
|
173 |
+
"mmlu_high_school_microeconomics": {
|
174 |
+
"alias": " - high_school_microeconomics",
|
175 |
+
"acc,none": 0.2605042016806723,
|
176 |
+
"acc_stderr,none": 0.028510251512341937
|
177 |
+
},
|
178 |
+
"mmlu_high_school_psychology": {
|
179 |
+
"alias": " - high_school_psychology",
|
180 |
+
"acc,none": 0.27155963302752295,
|
181 |
+
"acc_stderr,none": 0.019069098363191445
|
182 |
+
},
|
183 |
+
"mmlu_human_sexuality": {
|
184 |
+
"alias": " - human_sexuality",
|
185 |
+
"acc,none": 0.21374045801526717,
|
186 |
+
"acc_stderr,none": 0.0359546161177469
|
187 |
+
},
|
188 |
+
"mmlu_professional_psychology": {
|
189 |
+
"alias": " - professional_psychology",
|
190 |
+
"acc,none": 0.24183006535947713,
|
191 |
+
"acc_stderr,none": 0.017322789207784326
|
192 |
+
},
|
193 |
+
"mmlu_public_relations": {
|
194 |
+
"alias": " - public_relations",
|
195 |
+
"acc,none": 0.24545454545454545,
|
196 |
+
"acc_stderr,none": 0.041220665028782834
|
197 |
+
},
|
198 |
+
"mmlu_security_studies": {
|
199 |
+
"alias": " - security_studies",
|
200 |
+
"acc,none": 0.2612244897959184,
|
201 |
+
"acc_stderr,none": 0.028123429335142787
|
202 |
+
},
|
203 |
+
"mmlu_sociology": {
|
204 |
+
"alias": " - sociology",
|
205 |
+
"acc,none": 0.3034825870646766,
|
206 |
+
"acc_stderr,none": 0.03251006816458618
|
207 |
+
},
|
208 |
+
"mmlu_us_foreign_policy": {
|
209 |
+
"alias": " - us_foreign_policy",
|
210 |
+
"acc,none": 0.25,
|
211 |
+
"acc_stderr,none": 0.04351941398892446
|
212 |
+
},
|
213 |
+
"mmlu_stem": {
|
214 |
+
"alias": " - stem",
|
215 |
+
"acc,none": 0.25531240088804313,
|
216 |
+
"acc_stderr,none": 0.04558330291190535
|
217 |
+
},
|
218 |
+
"mmlu_abstract_algebra": {
|
219 |
+
"alias": " - abstract_algebra",
|
220 |
+
"acc,none": 0.26,
|
221 |
+
"acc_stderr,none": 0.0440844002276808
|
222 |
+
},
|
223 |
+
"mmlu_anatomy": {
|
224 |
+
"alias": " - anatomy",
|
225 |
+
"acc,none": 0.22962962962962963,
|
226 |
+
"acc_stderr,none": 0.03633384414073463
|
227 |
+
},
|
228 |
+
"mmlu_astronomy": {
|
229 |
+
"alias": " - astronomy",
|
230 |
+
"acc,none": 0.24342105263157895,
|
231 |
+
"acc_stderr,none": 0.034923496688842384
|
232 |
+
},
|
233 |
+
"mmlu_college_biology": {
|
234 |
+
"alias": " - college_biology",
|
235 |
+
"acc,none": 0.2777777777777778,
|
236 |
+
"acc_stderr,none": 0.03745554791462457
|
237 |
+
},
|
238 |
+
"mmlu_college_chemistry": {
|
239 |
+
"alias": " - college_chemistry",
|
240 |
+
"acc,none": 0.34,
|
241 |
+
"acc_stderr,none": 0.047609522856952344
|
242 |
+
},
|
243 |
+
"mmlu_college_computer_science": {
|
244 |
+
"alias": " - college_computer_science",
|
245 |
+
"acc,none": 0.25,
|
246 |
+
"acc_stderr,none": 0.04351941398892446
|
247 |
+
},
|
248 |
+
"mmlu_college_mathematics": {
|
249 |
+
"alias": " - college_mathematics",
|
250 |
+
"acc,none": 0.23,
|
251 |
+
"acc_stderr,none": 0.042295258468165044
|
252 |
+
},
|
253 |
+
"mmlu_college_physics": {
|
254 |
+
"alias": " - college_physics",
|
255 |
+
"acc,none": 0.2647058823529412,
|
256 |
+
"acc_stderr,none": 0.04389869956808778
|
257 |
+
},
|
258 |
+
"mmlu_computer_security": {
|
259 |
+
"alias": " - computer_security",
|
260 |
+
"acc,none": 0.22,
|
261 |
+
"acc_stderr,none": 0.041633319989322674
|
262 |
+
},
|
263 |
+
"mmlu_conceptual_physics": {
|
264 |
+
"alias": " - conceptual_physics",
|
265 |
+
"acc,none": 0.18723404255319148,
|
266 |
+
"acc_stderr,none": 0.025501588341883607
|
267 |
+
},
|
268 |
+
"mmlu_electrical_engineering": {
|
269 |
+
"alias": " - electrical_engineering",
|
270 |
+
"acc,none": 0.23448275862068965,
|
271 |
+
"acc_stderr,none": 0.035306258743465914
|
272 |
+
},
|
273 |
+
"mmlu_elementary_mathematics": {
|
274 |
+
"alias": " - elementary_mathematics",
|
275 |
+
"acc,none": 0.2962962962962963,
|
276 |
+
"acc_stderr,none": 0.023517294335963276
|
277 |
+
},
|
278 |
+
"mmlu_high_school_biology": {
|
279 |
+
"alias": " - high_school_biology",
|
280 |
+
"acc,none": 0.2903225806451613,
|
281 |
+
"acc_stderr,none": 0.025822106119415895
|
282 |
+
},
|
283 |
+
"mmlu_high_school_chemistry": {
|
284 |
+
"alias": " - high_school_chemistry",
|
285 |
+
"acc,none": 0.22167487684729065,
|
286 |
+
"acc_stderr,none": 0.029225575892489614
|
287 |
+
},
|
288 |
+
"mmlu_high_school_computer_science": {
|
289 |
+
"alias": " - high_school_computer_science",
|
290 |
+
"acc,none": 0.3,
|
291 |
+
"acc_stderr,none": 0.046056618647183814
|
292 |
+
},
|
293 |
+
"mmlu_high_school_mathematics": {
|
294 |
+
"alias": " - high_school_mathematics",
|
295 |
+
"acc,none": 0.2518518518518518,
|
296 |
+
"acc_stderr,none": 0.02646611753895991
|
297 |
+
},
|
298 |
+
"mmlu_high_school_physics": {
|
299 |
+
"alias": " - high_school_physics",
|
300 |
+
"acc,none": 0.2582781456953642,
|
301 |
+
"acc_stderr,none": 0.035737053147634576
|
302 |
+
},
|
303 |
+
"mmlu_high_school_statistics": {
|
304 |
+
"alias": " - high_school_statistics",
|
305 |
+
"acc,none": 0.25925925925925924,
|
306 |
+
"acc_stderr,none": 0.029886910547626964
|
307 |
+
},
|
308 |
+
"mmlu_machine_learning": {
|
309 |
+
"alias": " - machine_learning",
|
310 |
+
"acc,none": 0.19642857142857142,
|
311 |
+
"acc_stderr,none": 0.03770970049347019
|
312 |
+
}
|
313 |
+
},
|
314 |
+
"groups": {
|
315 |
+
"mmlu": {
|
316 |
+
"acc,none": 0.2525993448226748,
|
317 |
+
"acc_stderr,none": 0.04202282990456397,
|
318 |
+
"alias": "mmlu"
|
319 |
+
},
|
320 |
+
"mmlu_humanities": {
|
321 |
+
"alias": " - humanities",
|
322 |
+
"acc,none": 0.24017003188097769,
|
323 |
+
"acc_stderr,none": 0.02857393482131495
|
324 |
+
},
|
325 |
+
"mmlu_other": {
|
326 |
+
"alias": " - other",
|
327 |
+
"acc,none": 0.25683939491470875,
|
328 |
+
"acc_stderr,none": 0.05743915320464653
|
329 |
+
},
|
330 |
+
"mmlu_social_sciences": {
|
331 |
+
"alias": " - social_sciences",
|
332 |
+
"acc,none": 0.26454338641533964,
|
333 |
+
"acc_stderr,none": 0.034586953407146494
|
334 |
+
},
|
335 |
+
"mmlu_stem": {
|
336 |
+
"alias": " - stem",
|
337 |
+
"acc,none": 0.25531240088804313,
|
338 |
+
"acc_stderr,none": 0.04558330291190535
|
339 |
+
}
|
340 |
+
},
|
341 |
+
"configs": {
|
342 |
+
"mmlu_abstract_algebra": {
|
343 |
+
"task": "mmlu_abstract_algebra",
|
344 |
+
"task_alias": "abstract_algebra",
|
345 |
+
"group": "mmlu_stem",
|
346 |
+
"group_alias": "stem",
|
347 |
+
"dataset_path": "hails/mmlu_no_train",
|
348 |
+
"dataset_name": "abstract_algebra",
|
349 |
+
"test_split": "test",
|
350 |
+
"fewshot_split": "dev",
|
351 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
352 |
+
"doc_to_target": "answer",
|
353 |
+
"doc_to_choice": [
|
354 |
+
"A",
|
355 |
+
"B",
|
356 |
+
"C",
|
357 |
+
"D"
|
358 |
+
],
|
359 |
+
"description": "The following are multiple choice questions (with answers) about abstract algebra.\n\n",
|
360 |
+
"target_delimiter": " ",
|
361 |
+
"fewshot_delimiter": "\n\n",
|
362 |
+
"fewshot_config": {
|
363 |
+
"sampler": "first_n"
|
364 |
+
},
|
365 |
+
"metric_list": [
|
366 |
+
{
|
367 |
+
"metric": "acc",
|
368 |
+
"aggregation": "mean",
|
369 |
+
"higher_is_better": true
|
370 |
+
}
|
371 |
+
],
|
372 |
+
"output_type": "multiple_choice",
|
373 |
+
"repeats": 1,
|
374 |
+
"should_decontaminate": false,
|
375 |
+
"metadata": {
|
376 |
+
"version": 0.0
|
377 |
+
}
|
378 |
+
},
|
379 |
+
"mmlu_anatomy": {
|
380 |
+
"task": "mmlu_anatomy",
|
381 |
+
"task_alias": "anatomy",
|
382 |
+
"group": "mmlu_stem",
|
383 |
+
"group_alias": "stem",
|
384 |
+
"dataset_path": "hails/mmlu_no_train",
|
385 |
+
"dataset_name": "anatomy",
|
386 |
+
"test_split": "test",
|
387 |
+
"fewshot_split": "dev",
|
388 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
389 |
+
"doc_to_target": "answer",
|
390 |
+
"doc_to_choice": [
|
391 |
+
"A",
|
392 |
+
"B",
|
393 |
+
"C",
|
394 |
+
"D"
|
395 |
+
],
|
396 |
+
"description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
|
397 |
+
"target_delimiter": " ",
|
398 |
+
"fewshot_delimiter": "\n\n",
|
399 |
+
"fewshot_config": {
|
400 |
+
"sampler": "first_n"
|
401 |
+
},
|
402 |
+
"metric_list": [
|
403 |
+
{
|
404 |
+
"metric": "acc",
|
405 |
+
"aggregation": "mean",
|
406 |
+
"higher_is_better": true
|
407 |
+
}
|
408 |
+
],
|
409 |
+
"output_type": "multiple_choice",
|
410 |
+
"repeats": 1,
|
411 |
+
"should_decontaminate": false,
|
412 |
+
"metadata": {
|
413 |
+
"version": 0.0
|
414 |
+
}
|
415 |
+
},
|
416 |
+
"mmlu_astronomy": {
|
417 |
+
"task": "mmlu_astronomy",
|
418 |
+
"task_alias": "astronomy",
|
419 |
+
"group": "mmlu_stem",
|
420 |
+
"group_alias": "stem",
|
421 |
+
"dataset_path": "hails/mmlu_no_train",
|
422 |
+
"dataset_name": "astronomy",
|
423 |
+
"test_split": "test",
|
424 |
+
"fewshot_split": "dev",
|
425 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
426 |
+
"doc_to_target": "answer",
|
427 |
+
"doc_to_choice": [
|
428 |
+
"A",
|
429 |
+
"B",
|
430 |
+
"C",
|
431 |
+
"D"
|
432 |
+
],
|
433 |
+
"description": "The following are multiple choice questions (with answers) about astronomy.\n\n",
|
434 |
+
"target_delimiter": " ",
|
435 |
+
"fewshot_delimiter": "\n\n",
|
436 |
+
"fewshot_config": {
|
437 |
+
"sampler": "first_n"
|
438 |
+
},
|
439 |
+
"metric_list": [
|
440 |
+
{
|
441 |
+
"metric": "acc",
|
442 |
+
"aggregation": "mean",
|
443 |
+
"higher_is_better": true
|
444 |
+
}
|
445 |
+
],
|
446 |
+
"output_type": "multiple_choice",
|
447 |
+
"repeats": 1,
|
448 |
+
"should_decontaminate": false,
|
449 |
+
"metadata": {
|
450 |
+
"version": 0.0
|
451 |
+
}
|
452 |
+
},
|
453 |
+
"mmlu_business_ethics": {
|
454 |
+
"task": "mmlu_business_ethics",
|
455 |
+
"task_alias": "business_ethics",
|
456 |
+
"group": "mmlu_other",
|
457 |
+
"group_alias": "other",
|
458 |
+
"dataset_path": "hails/mmlu_no_train",
|
459 |
+
"dataset_name": "business_ethics",
|
460 |
+
"test_split": "test",
|
461 |
+
"fewshot_split": "dev",
|
462 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
463 |
+
"doc_to_target": "answer",
|
464 |
+
"doc_to_choice": [
|
465 |
+
"A",
|
466 |
+
"B",
|
467 |
+
"C",
|
468 |
+
"D"
|
469 |
+
],
|
470 |
+
"description": "The following are multiple choice questions (with answers) about business ethics.\n\n",
|
471 |
+
"target_delimiter": " ",
|
472 |
+
"fewshot_delimiter": "\n\n",
|
473 |
+
"fewshot_config": {
|
474 |
+
"sampler": "first_n"
|
475 |
+
},
|
476 |
+
"metric_list": [
|
477 |
+
{
|
478 |
+
"metric": "acc",
|
479 |
+
"aggregation": "mean",
|
480 |
+
"higher_is_better": true
|
481 |
+
}
|
482 |
+
],
|
483 |
+
"output_type": "multiple_choice",
|
484 |
+
"repeats": 1,
|
485 |
+
"should_decontaminate": false,
|
486 |
+
"metadata": {
|
487 |
+
"version": 0.0
|
488 |
+
}
|
489 |
+
},
|
490 |
+
"mmlu_clinical_knowledge": {
|
491 |
+
"task": "mmlu_clinical_knowledge",
|
492 |
+
"task_alias": "clinical_knowledge",
|
493 |
+
"group": "mmlu_other",
|
494 |
+
"group_alias": "other",
|
495 |
+
"dataset_path": "hails/mmlu_no_train",
|
496 |
+
"dataset_name": "clinical_knowledge",
|
497 |
+
"test_split": "test",
|
498 |
+
"fewshot_split": "dev",
|
499 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
500 |
+
"doc_to_target": "answer",
|
501 |
+
"doc_to_choice": [
|
502 |
+
"A",
|
503 |
+
"B",
|
504 |
+
"C",
|
505 |
+
"D"
|
506 |
+
],
|
507 |
+
"description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
|
508 |
+
"target_delimiter": " ",
|
509 |
+
"fewshot_delimiter": "\n\n",
|
510 |
+
"fewshot_config": {
|
511 |
+
"sampler": "first_n"
|
512 |
+
},
|
513 |
+
"metric_list": [
|
514 |
+
{
|
515 |
+
"metric": "acc",
|
516 |
+
"aggregation": "mean",
|
517 |
+
"higher_is_better": true
|
518 |
+
}
|
519 |
+
],
|
520 |
+
"output_type": "multiple_choice",
|
521 |
+
"repeats": 1,
|
522 |
+
"should_decontaminate": false,
|
523 |
+
"metadata": {
|
524 |
+
"version": 0.0
|
525 |
+
}
|
526 |
+
},
|
527 |
+
"mmlu_college_biology": {
|
528 |
+
"task": "mmlu_college_biology",
|
529 |
+
"task_alias": "college_biology",
|
530 |
+
"group": "mmlu_stem",
|
531 |
+
"group_alias": "stem",
|
532 |
+
"dataset_path": "hails/mmlu_no_train",
|
533 |
+
"dataset_name": "college_biology",
|
534 |
+
"test_split": "test",
|
535 |
+
"fewshot_split": "dev",
|
536 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
537 |
+
"doc_to_target": "answer",
|
538 |
+
"doc_to_choice": [
|
539 |
+
"A",
|
540 |
+
"B",
|
541 |
+
"C",
|
542 |
+
"D"
|
543 |
+
],
|
544 |
+
"description": "The following are multiple choice questions (with answers) about college biology.\n\n",
|
545 |
+
"target_delimiter": " ",
|
546 |
+
"fewshot_delimiter": "\n\n",
|
547 |
+
"fewshot_config": {
|
548 |
+
"sampler": "first_n"
|
549 |
+
},
|
550 |
+
"metric_list": [
|
551 |
+
{
|
552 |
+
"metric": "acc",
|
553 |
+
"aggregation": "mean",
|
554 |
+
"higher_is_better": true
|
555 |
+
}
|
556 |
+
],
|
557 |
+
"output_type": "multiple_choice",
|
558 |
+
"repeats": 1,
|
559 |
+
"should_decontaminate": false,
|
560 |
+
"metadata": {
|
561 |
+
"version": 0.0
|
562 |
+
}
|
563 |
+
},
|
564 |
+
"mmlu_college_chemistry": {
|
565 |
+
"task": "mmlu_college_chemistry",
|
566 |
+
"task_alias": "college_chemistry",
|
567 |
+
"group": "mmlu_stem",
|
568 |
+
"group_alias": "stem",
|
569 |
+
"dataset_path": "hails/mmlu_no_train",
|
570 |
+
"dataset_name": "college_chemistry",
|
571 |
+
"test_split": "test",
|
572 |
+
"fewshot_split": "dev",
|
573 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
574 |
+
"doc_to_target": "answer",
|
575 |
+
"doc_to_choice": [
|
576 |
+
"A",
|
577 |
+
"B",
|
578 |
+
"C",
|
579 |
+
"D"
|
580 |
+
],
|
581 |
+
"description": "The following are multiple choice questions (with answers) about college chemistry.\n\n",
|
582 |
+
"target_delimiter": " ",
|
583 |
+
"fewshot_delimiter": "\n\n",
|
584 |
+
"fewshot_config": {
|
585 |
+
"sampler": "first_n"
|
586 |
+
},
|
587 |
+
"metric_list": [
|
588 |
+
{
|
589 |
+
"metric": "acc",
|
590 |
+
"aggregation": "mean",
|
591 |
+
"higher_is_better": true
|
592 |
+
}
|
593 |
+
],
|
594 |
+
"output_type": "multiple_choice",
|
595 |
+
"repeats": 1,
|
596 |
+
"should_decontaminate": false,
|
597 |
+
"metadata": {
|
598 |
+
"version": 0.0
|
599 |
+
}
|
600 |
+
},
|
601 |
+
"mmlu_college_computer_science": {
|
602 |
+
"task": "mmlu_college_computer_science",
|
603 |
+
"task_alias": "college_computer_science",
|
604 |
+
"group": "mmlu_stem",
|
605 |
+
"group_alias": "stem",
|
606 |
+
"dataset_path": "hails/mmlu_no_train",
|
607 |
+
"dataset_name": "college_computer_science",
|
608 |
+
"test_split": "test",
|
609 |
+
"fewshot_split": "dev",
|
610 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
611 |
+
"doc_to_target": "answer",
|
612 |
+
"doc_to_choice": [
|
613 |
+
"A",
|
614 |
+
"B",
|
615 |
+
"C",
|
616 |
+
"D"
|
617 |
+
],
|
618 |
+
"description": "The following are multiple choice questions (with answers) about college computer science.\n\n",
|
619 |
+
"target_delimiter": " ",
|
620 |
+
"fewshot_delimiter": "\n\n",
|
621 |
+
"fewshot_config": {
|
622 |
+
"sampler": "first_n"
|
623 |
+
},
|
624 |
+
"metric_list": [
|
625 |
+
{
|
626 |
+
"metric": "acc",
|
627 |
+
"aggregation": "mean",
|
628 |
+
"higher_is_better": true
|
629 |
+
}
|
630 |
+
],
|
631 |
+
"output_type": "multiple_choice",
|
632 |
+
"repeats": 1,
|
633 |
+
"should_decontaminate": false,
|
634 |
+
"metadata": {
|
635 |
+
"version": 0.0
|
636 |
+
}
|
637 |
+
},
|
638 |
+
"mmlu_college_mathematics": {
|
639 |
+
"task": "mmlu_college_mathematics",
|
640 |
+
"task_alias": "college_mathematics",
|
641 |
+
"group": "mmlu_stem",
|
642 |
+
"group_alias": "stem",
|
643 |
+
"dataset_path": "hails/mmlu_no_train",
|
644 |
+
"dataset_name": "college_mathematics",
|
645 |
+
"test_split": "test",
|
646 |
+
"fewshot_split": "dev",
|
647 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
648 |
+
"doc_to_target": "answer",
|
649 |
+
"doc_to_choice": [
|
650 |
+
"A",
|
651 |
+
"B",
|
652 |
+
"C",
|
653 |
+
"D"
|
654 |
+
],
|
655 |
+
"description": "The following are multiple choice questions (with answers) about college mathematics.\n\n",
|
656 |
+
"target_delimiter": " ",
|
657 |
+
"fewshot_delimiter": "\n\n",
|
658 |
+
"fewshot_config": {
|
659 |
+
"sampler": "first_n"
|
660 |
+
},
|
661 |
+
"metric_list": [
|
662 |
+
{
|
663 |
+
"metric": "acc",
|
664 |
+
"aggregation": "mean",
|
665 |
+
"higher_is_better": true
|
666 |
+
}
|
667 |
+
],
|
668 |
+
"output_type": "multiple_choice",
|
669 |
+
"repeats": 1,
|
670 |
+
"should_decontaminate": false,
|
671 |
+
"metadata": {
|
672 |
+
"version": 0.0
|
673 |
+
}
|
674 |
+
},
|
675 |
+
"mmlu_college_medicine": {
|
676 |
+
"task": "mmlu_college_medicine",
|
677 |
+
"task_alias": "college_medicine",
|
678 |
+
"group": "mmlu_other",
|
679 |
+
"group_alias": "other",
|
680 |
+
"dataset_path": "hails/mmlu_no_train",
|
681 |
+
"dataset_name": "college_medicine",
|
682 |
+
"test_split": "test",
|
683 |
+
"fewshot_split": "dev",
|
684 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
685 |
+
"doc_to_target": "answer",
|
686 |
+
"doc_to_choice": [
|
687 |
+
"A",
|
688 |
+
"B",
|
689 |
+
"C",
|
690 |
+
"D"
|
691 |
+
],
|
692 |
+
"description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
|
693 |
+
"target_delimiter": " ",
|
694 |
+
"fewshot_delimiter": "\n\n",
|
695 |
+
"fewshot_config": {
|
696 |
+
"sampler": "first_n"
|
697 |
+
},
|
698 |
+
"metric_list": [
|
699 |
+
{
|
700 |
+
"metric": "acc",
|
701 |
+
"aggregation": "mean",
|
702 |
+
"higher_is_better": true
|
703 |
+
}
|
704 |
+
],
|
705 |
+
"output_type": "multiple_choice",
|
706 |
+
"repeats": 1,
|
707 |
+
"should_decontaminate": false,
|
708 |
+
"metadata": {
|
709 |
+
"version": 0.0
|
710 |
+
}
|
711 |
+
},
|
712 |
+
"mmlu_college_physics": {
|
713 |
+
"task": "mmlu_college_physics",
|
714 |
+
"task_alias": "college_physics",
|
715 |
+
"group": "mmlu_stem",
|
716 |
+
"group_alias": "stem",
|
717 |
+
"dataset_path": "hails/mmlu_no_train",
|
718 |
+
"dataset_name": "college_physics",
|
719 |
+
"test_split": "test",
|
720 |
+
"fewshot_split": "dev",
|
721 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
722 |
+
"doc_to_target": "answer",
|
723 |
+
"doc_to_choice": [
|
724 |
+
"A",
|
725 |
+
"B",
|
726 |
+
"C",
|
727 |
+
"D"
|
728 |
+
],
|
729 |
+
"description": "The following are multiple choice questions (with answers) about college physics.\n\n",
|
730 |
+
"target_delimiter": " ",
|
731 |
+
"fewshot_delimiter": "\n\n",
|
732 |
+
"fewshot_config": {
|
733 |
+
"sampler": "first_n"
|
734 |
+
},
|
735 |
+
"metric_list": [
|
736 |
+
{
|
737 |
+
"metric": "acc",
|
738 |
+
"aggregation": "mean",
|
739 |
+
"higher_is_better": true
|
740 |
+
}
|
741 |
+
],
|
742 |
+
"output_type": "multiple_choice",
|
743 |
+
"repeats": 1,
|
744 |
+
"should_decontaminate": false,
|
745 |
+
"metadata": {
|
746 |
+
"version": 0.0
|
747 |
+
}
|
748 |
+
},
|
749 |
+
"mmlu_computer_security": {
|
750 |
+
"task": "mmlu_computer_security",
|
751 |
+
"task_alias": "computer_security",
|
752 |
+
"group": "mmlu_stem",
|
753 |
+
"group_alias": "stem",
|
754 |
+
"dataset_path": "hails/mmlu_no_train",
|
755 |
+
"dataset_name": "computer_security",
|
756 |
+
"test_split": "test",
|
757 |
+
"fewshot_split": "dev",
|
758 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
759 |
+
"doc_to_target": "answer",
|
760 |
+
"doc_to_choice": [
|
761 |
+
"A",
|
762 |
+
"B",
|
763 |
+
"C",
|
764 |
+
"D"
|
765 |
+
],
|
766 |
+
"description": "The following are multiple choice questions (with answers) about computer security.\n\n",
|
767 |
+
"target_delimiter": " ",
|
768 |
+
"fewshot_delimiter": "\n\n",
|
769 |
+
"fewshot_config": {
|
770 |
+
"sampler": "first_n"
|
771 |
+
},
|
772 |
+
"metric_list": [
|
773 |
+
{
|
774 |
+
"metric": "acc",
|
775 |
+
"aggregation": "mean",
|
776 |
+
"higher_is_better": true
|
777 |
+
}
|
778 |
+
],
|
779 |
+
"output_type": "multiple_choice",
|
780 |
+
"repeats": 1,
|
781 |
+
"should_decontaminate": false,
|
782 |
+
"metadata": {
|
783 |
+
"version": 0.0
|
784 |
+
}
|
785 |
+
},
|
786 |
+
"mmlu_conceptual_physics": {
|
787 |
+
"task": "mmlu_conceptual_physics",
|
788 |
+
"task_alias": "conceptual_physics",
|
789 |
+
"group": "mmlu_stem",
|
790 |
+
"group_alias": "stem",
|
791 |
+
"dataset_path": "hails/mmlu_no_train",
|
792 |
+
"dataset_name": "conceptual_physics",
|
793 |
+
"test_split": "test",
|
794 |
+
"fewshot_split": "dev",
|
795 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
796 |
+
"doc_to_target": "answer",
|
797 |
+
"doc_to_choice": [
|
798 |
+
"A",
|
799 |
+
"B",
|
800 |
+
"C",
|
801 |
+
"D"
|
802 |
+
],
|
803 |
+
"description": "The following are multiple choice questions (with answers) about conceptual physics.\n\n",
|
804 |
+
"target_delimiter": " ",
|
805 |
+
"fewshot_delimiter": "\n\n",
|
806 |
+
"fewshot_config": {
|
807 |
+
"sampler": "first_n"
|
808 |
+
},
|
809 |
+
"metric_list": [
|
810 |
+
{
|
811 |
+
"metric": "acc",
|
812 |
+
"aggregation": "mean",
|
813 |
+
"higher_is_better": true
|
814 |
+
}
|
815 |
+
],
|
816 |
+
"output_type": "multiple_choice",
|
817 |
+
"repeats": 1,
|
818 |
+
"should_decontaminate": false,
|
819 |
+
"metadata": {
|
820 |
+
"version": 0.0
|
821 |
+
}
|
822 |
+
},
|
823 |
+
"mmlu_econometrics": {
|
824 |
+
"task": "mmlu_econometrics",
|
825 |
+
"task_alias": "econometrics",
|
826 |
+
"group": "mmlu_social_sciences",
|
827 |
+
"group_alias": "social_sciences",
|
828 |
+
"dataset_path": "hails/mmlu_no_train",
|
829 |
+
"dataset_name": "econometrics",
|
830 |
+
"test_split": "test",
|
831 |
+
"fewshot_split": "dev",
|
832 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
833 |
+
"doc_to_target": "answer",
|
834 |
+
"doc_to_choice": [
|
835 |
+
"A",
|
836 |
+
"B",
|
837 |
+
"C",
|
838 |
+
"D"
|
839 |
+
],
|
840 |
+
"description": "The following are multiple choice questions (with answers) about econometrics.\n\n",
|
841 |
+
"target_delimiter": " ",
|
842 |
+
"fewshot_delimiter": "\n\n",
|
843 |
+
"fewshot_config": {
|
844 |
+
"sampler": "first_n"
|
845 |
+
},
|
846 |
+
"metric_list": [
|
847 |
+
{
|
848 |
+
"metric": "acc",
|
849 |
+
"aggregation": "mean",
|
850 |
+
"higher_is_better": true
|
851 |
+
}
|
852 |
+
],
|
853 |
+
"output_type": "multiple_choice",
|
854 |
+
"repeats": 1,
|
855 |
+
"should_decontaminate": false,
|
856 |
+
"metadata": {
|
857 |
+
"version": 0.0
|
858 |
+
}
|
859 |
+
},
|
860 |
+
"mmlu_electrical_engineering": {
|
861 |
+
"task": "mmlu_electrical_engineering",
|
862 |
+
"task_alias": "electrical_engineering",
|
863 |
+
"group": "mmlu_stem",
|
864 |
+
"group_alias": "stem",
|
865 |
+
"dataset_path": "hails/mmlu_no_train",
|
866 |
+
"dataset_name": "electrical_engineering",
|
867 |
+
"test_split": "test",
|
868 |
+
"fewshot_split": "dev",
|
869 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
870 |
+
"doc_to_target": "answer",
|
871 |
+
"doc_to_choice": [
|
872 |
+
"A",
|
873 |
+
"B",
|
874 |
+
"C",
|
875 |
+
"D"
|
876 |
+
],
|
877 |
+
"description": "The following are multiple choice questions (with answers) about electrical engineering.\n\n",
|
878 |
+
"target_delimiter": " ",
|
879 |
+
"fewshot_delimiter": "\n\n",
|
880 |
+
"fewshot_config": {
|
881 |
+
"sampler": "first_n"
|
882 |
+
},
|
883 |
+
"metric_list": [
|
884 |
+
{
|
885 |
+
"metric": "acc",
|
886 |
+
"aggregation": "mean",
|
887 |
+
"higher_is_better": true
|
888 |
+
}
|
889 |
+
],
|
890 |
+
"output_type": "multiple_choice",
|
891 |
+
"repeats": 1,
|
892 |
+
"should_decontaminate": false,
|
893 |
+
"metadata": {
|
894 |
+
"version": 0.0
|
895 |
+
}
|
896 |
+
},
|
897 |
+
"mmlu_elementary_mathematics": {
|
898 |
+
"task": "mmlu_elementary_mathematics",
|
899 |
+
"task_alias": "elementary_mathematics",
|
900 |
+
"group": "mmlu_stem",
|
901 |
+
"group_alias": "stem",
|
902 |
+
"dataset_path": "hails/mmlu_no_train",
|
903 |
+
"dataset_name": "elementary_mathematics",
|
904 |
+
"test_split": "test",
|
905 |
+
"fewshot_split": "dev",
|
906 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
907 |
+
"doc_to_target": "answer",
|
908 |
+
"doc_to_choice": [
|
909 |
+
"A",
|
910 |
+
"B",
|
911 |
+
"C",
|
912 |
+
"D"
|
913 |
+
],
|
914 |
+
"description": "The following are multiple choice questions (with answers) about elementary mathematics.\n\n",
|
915 |
+
"target_delimiter": " ",
|
916 |
+
"fewshot_delimiter": "\n\n",
|
917 |
+
"fewshot_config": {
|
918 |
+
"sampler": "first_n"
|
919 |
+
},
|
920 |
+
"metric_list": [
|
921 |
+
{
|
922 |
+
"metric": "acc",
|
923 |
+
"aggregation": "mean",
|
924 |
+
"higher_is_better": true
|
925 |
+
}
|
926 |
+
],
|
927 |
+
"output_type": "multiple_choice",
|
928 |
+
"repeats": 1,
|
929 |
+
"should_decontaminate": false,
|
930 |
+
"metadata": {
|
931 |
+
"version": 0.0
|
932 |
+
}
|
933 |
+
},
|
934 |
+
"mmlu_formal_logic": {
|
935 |
+
"task": "mmlu_formal_logic",
|
936 |
+
"task_alias": "formal_logic",
|
937 |
+
"group": "mmlu_humanities",
|
938 |
+
"group_alias": "humanities",
|
939 |
+
"dataset_path": "hails/mmlu_no_train",
|
940 |
+
"dataset_name": "formal_logic",
|
941 |
+
"test_split": "test",
|
942 |
+
"fewshot_split": "dev",
|
943 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
944 |
+
"doc_to_target": "answer",
|
945 |
+
"doc_to_choice": [
|
946 |
+
"A",
|
947 |
+
"B",
|
948 |
+
"C",
|
949 |
+
"D"
|
950 |
+
],
|
951 |
+
"description": "The following are multiple choice questions (with answers) about formal logic.\n\n",
|
952 |
+
"target_delimiter": " ",
|
953 |
+
"fewshot_delimiter": "\n\n",
|
954 |
+
"fewshot_config": {
|
955 |
+
"sampler": "first_n"
|
956 |
+
},
|
957 |
+
"metric_list": [
|
958 |
+
{
|
959 |
+
"metric": "acc",
|
960 |
+
"aggregation": "mean",
|
961 |
+
"higher_is_better": true
|
962 |
+
}
|
963 |
+
],
|
964 |
+
"output_type": "multiple_choice",
|
965 |
+
"repeats": 1,
|
966 |
+
"should_decontaminate": false,
|
967 |
+
"metadata": {
|
968 |
+
"version": 0.0
|
969 |
+
}
|
970 |
+
},
|
971 |
+
"mmlu_global_facts": {
|
972 |
+
"task": "mmlu_global_facts",
|
973 |
+
"task_alias": "global_facts",
|
974 |
+
"group": "mmlu_other",
|
975 |
+
"group_alias": "other",
|
976 |
+
"dataset_path": "hails/mmlu_no_train",
|
977 |
+
"dataset_name": "global_facts",
|
978 |
+
"test_split": "test",
|
979 |
+
"fewshot_split": "dev",
|
980 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
981 |
+
"doc_to_target": "answer",
|
982 |
+
"doc_to_choice": [
|
983 |
+
"A",
|
984 |
+
"B",
|
985 |
+
"C",
|
986 |
+
"D"
|
987 |
+
],
|
988 |
+
"description": "The following are multiple choice questions (with answers) about global facts.\n\n",
|
989 |
+
"target_delimiter": " ",
|
990 |
+
"fewshot_delimiter": "\n\n",
|
991 |
+
"fewshot_config": {
|
992 |
+
"sampler": "first_n"
|
993 |
+
},
|
994 |
+
"metric_list": [
|
995 |
+
{
|
996 |
+
"metric": "acc",
|
997 |
+
"aggregation": "mean",
|
998 |
+
"higher_is_better": true
|
999 |
+
}
|
1000 |
+
],
|
1001 |
+
"output_type": "multiple_choice",
|
1002 |
+
"repeats": 1,
|
1003 |
+
"should_decontaminate": false,
|
1004 |
+
"metadata": {
|
1005 |
+
"version": 0.0
|
1006 |
+
}
|
1007 |
+
},
|
1008 |
+
"mmlu_high_school_biology": {
|
1009 |
+
"task": "mmlu_high_school_biology",
|
1010 |
+
"task_alias": "high_school_biology",
|
1011 |
+
"group": "mmlu_stem",
|
1012 |
+
"group_alias": "stem",
|
1013 |
+
"dataset_path": "hails/mmlu_no_train",
|
1014 |
+
"dataset_name": "high_school_biology",
|
1015 |
+
"test_split": "test",
|
1016 |
+
"fewshot_split": "dev",
|
1017 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1018 |
+
"doc_to_target": "answer",
|
1019 |
+
"doc_to_choice": [
|
1020 |
+
"A",
|
1021 |
+
"B",
|
1022 |
+
"C",
|
1023 |
+
"D"
|
1024 |
+
],
|
1025 |
+
"description": "The following are multiple choice questions (with answers) about high school biology.\n\n",
|
1026 |
+
"target_delimiter": " ",
|
1027 |
+
"fewshot_delimiter": "\n\n",
|
1028 |
+
"fewshot_config": {
|
1029 |
+
"sampler": "first_n"
|
1030 |
+
},
|
1031 |
+
"metric_list": [
|
1032 |
+
{
|
1033 |
+
"metric": "acc",
|
1034 |
+
"aggregation": "mean",
|
1035 |
+
"higher_is_better": true
|
1036 |
+
}
|
1037 |
+
],
|
1038 |
+
"output_type": "multiple_choice",
|
1039 |
+
"repeats": 1,
|
1040 |
+
"should_decontaminate": false,
|
1041 |
+
"metadata": {
|
1042 |
+
"version": 0.0
|
1043 |
+
}
|
1044 |
+
},
|
1045 |
+
"mmlu_high_school_chemistry": {
|
1046 |
+
"task": "mmlu_high_school_chemistry",
|
1047 |
+
"task_alias": "high_school_chemistry",
|
1048 |
+
"group": "mmlu_stem",
|
1049 |
+
"group_alias": "stem",
|
1050 |
+
"dataset_path": "hails/mmlu_no_train",
|
1051 |
+
"dataset_name": "high_school_chemistry",
|
1052 |
+
"test_split": "test",
|
1053 |
+
"fewshot_split": "dev",
|
1054 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1055 |
+
"doc_to_target": "answer",
|
1056 |
+
"doc_to_choice": [
|
1057 |
+
"A",
|
1058 |
+
"B",
|
1059 |
+
"C",
|
1060 |
+
"D"
|
1061 |
+
],
|
1062 |
+
"description": "The following are multiple choice questions (with answers) about high school chemistry.\n\n",
|
1063 |
+
"target_delimiter": " ",
|
1064 |
+
"fewshot_delimiter": "\n\n",
|
1065 |
+
"fewshot_config": {
|
1066 |
+
"sampler": "first_n"
|
1067 |
+
},
|
1068 |
+
"metric_list": [
|
1069 |
+
{
|
1070 |
+
"metric": "acc",
|
1071 |
+
"aggregation": "mean",
|
1072 |
+
"higher_is_better": true
|
1073 |
+
}
|
1074 |
+
],
|
1075 |
+
"output_type": "multiple_choice",
|
1076 |
+
"repeats": 1,
|
1077 |
+
"should_decontaminate": false,
|
1078 |
+
"metadata": {
|
1079 |
+
"version": 0.0
|
1080 |
+
}
|
1081 |
+
},
|
1082 |
+
"mmlu_high_school_computer_science": {
|
1083 |
+
"task": "mmlu_high_school_computer_science",
|
1084 |
+
"task_alias": "high_school_computer_science",
|
1085 |
+
"group": "mmlu_stem",
|
1086 |
+
"group_alias": "stem",
|
1087 |
+
"dataset_path": "hails/mmlu_no_train",
|
1088 |
+
"dataset_name": "high_school_computer_science",
|
1089 |
+
"test_split": "test",
|
1090 |
+
"fewshot_split": "dev",
|
1091 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1092 |
+
"doc_to_target": "answer",
|
1093 |
+
"doc_to_choice": [
|
1094 |
+
"A",
|
1095 |
+
"B",
|
1096 |
+
"C",
|
1097 |
+
"D"
|
1098 |
+
],
|
1099 |
+
"description": "The following are multiple choice questions (with answers) about high school computer science.\n\n",
|
1100 |
+
"target_delimiter": " ",
|
1101 |
+
"fewshot_delimiter": "\n\n",
|
1102 |
+
"fewshot_config": {
|
1103 |
+
"sampler": "first_n"
|
1104 |
+
},
|
1105 |
+
"metric_list": [
|
1106 |
+
{
|
1107 |
+
"metric": "acc",
|
1108 |
+
"aggregation": "mean",
|
1109 |
+
"higher_is_better": true
|
1110 |
+
}
|
1111 |
+
],
|
1112 |
+
"output_type": "multiple_choice",
|
1113 |
+
"repeats": 1,
|
1114 |
+
"should_decontaminate": false,
|
1115 |
+
"metadata": {
|
1116 |
+
"version": 0.0
|
1117 |
+
}
|
1118 |
+
},
|
1119 |
+
"mmlu_high_school_european_history": {
|
1120 |
+
"task": "mmlu_high_school_european_history",
|
1121 |
+
"task_alias": "high_school_european_history",
|
1122 |
+
"group": "mmlu_humanities",
|
1123 |
+
"group_alias": "humanities",
|
1124 |
+
"dataset_path": "hails/mmlu_no_train",
|
1125 |
+
"dataset_name": "high_school_european_history",
|
1126 |
+
"test_split": "test",
|
1127 |
+
"fewshot_split": "dev",
|
1128 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1129 |
+
"doc_to_target": "answer",
|
1130 |
+
"doc_to_choice": [
|
1131 |
+
"A",
|
1132 |
+
"B",
|
1133 |
+
"C",
|
1134 |
+
"D"
|
1135 |
+
],
|
1136 |
+
"description": "The following are multiple choice questions (with answers) about high school european history.\n\n",
|
1137 |
+
"target_delimiter": " ",
|
1138 |
+
"fewshot_delimiter": "\n\n",
|
1139 |
+
"fewshot_config": {
|
1140 |
+
"sampler": "first_n"
|
1141 |
+
},
|
1142 |
+
"metric_list": [
|
1143 |
+
{
|
1144 |
+
"metric": "acc",
|
1145 |
+
"aggregation": "mean",
|
1146 |
+
"higher_is_better": true
|
1147 |
+
}
|
1148 |
+
],
|
1149 |
+
"output_type": "multiple_choice",
|
1150 |
+
"repeats": 1,
|
1151 |
+
"should_decontaminate": false,
|
1152 |
+
"metadata": {
|
1153 |
+
"version": 0.0
|
1154 |
+
}
|
1155 |
+
},
|
1156 |
+
"mmlu_high_school_geography": {
|
1157 |
+
"task": "mmlu_high_school_geography",
|
1158 |
+
"task_alias": "high_school_geography",
|
1159 |
+
"group": "mmlu_social_sciences",
|
1160 |
+
"group_alias": "social_sciences",
|
1161 |
+
"dataset_path": "hails/mmlu_no_train",
|
1162 |
+
"dataset_name": "high_school_geography",
|
1163 |
+
"test_split": "test",
|
1164 |
+
"fewshot_split": "dev",
|
1165 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1166 |
+
"doc_to_target": "answer",
|
1167 |
+
"doc_to_choice": [
|
1168 |
+
"A",
|
1169 |
+
"B",
|
1170 |
+
"C",
|
1171 |
+
"D"
|
1172 |
+
],
|
1173 |
+
"description": "The following are multiple choice questions (with answers) about high school geography.\n\n",
|
1174 |
+
"target_delimiter": " ",
|
1175 |
+
"fewshot_delimiter": "\n\n",
|
1176 |
+
"fewshot_config": {
|
1177 |
+
"sampler": "first_n"
|
1178 |
+
},
|
1179 |
+
"metric_list": [
|
1180 |
+
{
|
1181 |
+
"metric": "acc",
|
1182 |
+
"aggregation": "mean",
|
1183 |
+
"higher_is_better": true
|
1184 |
+
}
|
1185 |
+
],
|
1186 |
+
"output_type": "multiple_choice",
|
1187 |
+
"repeats": 1,
|
1188 |
+
"should_decontaminate": false,
|
1189 |
+
"metadata": {
|
1190 |
+
"version": 0.0
|
1191 |
+
}
|
1192 |
+
},
|
1193 |
+
"mmlu_high_school_government_and_politics": {
|
1194 |
+
"task": "mmlu_high_school_government_and_politics",
|
1195 |
+
"task_alias": "high_school_government_and_politics",
|
1196 |
+
"group": "mmlu_social_sciences",
|
1197 |
+
"group_alias": "social_sciences",
|
1198 |
+
"dataset_path": "hails/mmlu_no_train",
|
1199 |
+
"dataset_name": "high_school_government_and_politics",
|
1200 |
+
"test_split": "test",
|
1201 |
+
"fewshot_split": "dev",
|
1202 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1203 |
+
"doc_to_target": "answer",
|
1204 |
+
"doc_to_choice": [
|
1205 |
+
"A",
|
1206 |
+
"B",
|
1207 |
+
"C",
|
1208 |
+
"D"
|
1209 |
+
],
|
1210 |
+
"description": "The following are multiple choice questions (with answers) about high school government and politics.\n\n",
|
1211 |
+
"target_delimiter": " ",
|
1212 |
+
"fewshot_delimiter": "\n\n",
|
1213 |
+
"fewshot_config": {
|
1214 |
+
"sampler": "first_n"
|
1215 |
+
},
|
1216 |
+
"metric_list": [
|
1217 |
+
{
|
1218 |
+
"metric": "acc",
|
1219 |
+
"aggregation": "mean",
|
1220 |
+
"higher_is_better": true
|
1221 |
+
}
|
1222 |
+
],
|
1223 |
+
"output_type": "multiple_choice",
|
1224 |
+
"repeats": 1,
|
1225 |
+
"should_decontaminate": false,
|
1226 |
+
"metadata": {
|
1227 |
+
"version": 0.0
|
1228 |
+
}
|
1229 |
+
},
|
1230 |
+
"mmlu_high_school_macroeconomics": {
|
1231 |
+
"task": "mmlu_high_school_macroeconomics",
|
1232 |
+
"task_alias": "high_school_macroeconomics",
|
1233 |
+
"group": "mmlu_social_sciences",
|
1234 |
+
"group_alias": "social_sciences",
|
1235 |
+
"dataset_path": "hails/mmlu_no_train",
|
1236 |
+
"dataset_name": "high_school_macroeconomics",
|
1237 |
+
"test_split": "test",
|
1238 |
+
"fewshot_split": "dev",
|
1239 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1240 |
+
"doc_to_target": "answer",
|
1241 |
+
"doc_to_choice": [
|
1242 |
+
"A",
|
1243 |
+
"B",
|
1244 |
+
"C",
|
1245 |
+
"D"
|
1246 |
+
],
|
1247 |
+
"description": "The following are multiple choice questions (with answers) about high school macroeconomics.\n\n",
|
1248 |
+
"target_delimiter": " ",
|
1249 |
+
"fewshot_delimiter": "\n\n",
|
1250 |
+
"fewshot_config": {
|
1251 |
+
"sampler": "first_n"
|
1252 |
+
},
|
1253 |
+
"metric_list": [
|
1254 |
+
{
|
1255 |
+
"metric": "acc",
|
1256 |
+
"aggregation": "mean",
|
1257 |
+
"higher_is_better": true
|
1258 |
+
}
|
1259 |
+
],
|
1260 |
+
"output_type": "multiple_choice",
|
1261 |
+
"repeats": 1,
|
1262 |
+
"should_decontaminate": false,
|
1263 |
+
"metadata": {
|
1264 |
+
"version": 0.0
|
1265 |
+
}
|
1266 |
+
},
|
1267 |
+
"mmlu_high_school_mathematics": {
|
1268 |
+
"task": "mmlu_high_school_mathematics",
|
1269 |
+
"task_alias": "high_school_mathematics",
|
1270 |
+
"group": "mmlu_stem",
|
1271 |
+
"group_alias": "stem",
|
1272 |
+
"dataset_path": "hails/mmlu_no_train",
|
1273 |
+
"dataset_name": "high_school_mathematics",
|
1274 |
+
"test_split": "test",
|
1275 |
+
"fewshot_split": "dev",
|
1276 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1277 |
+
"doc_to_target": "answer",
|
1278 |
+
"doc_to_choice": [
|
1279 |
+
"A",
|
1280 |
+
"B",
|
1281 |
+
"C",
|
1282 |
+
"D"
|
1283 |
+
],
|
1284 |
+
"description": "The following are multiple choice questions (with answers) about high school mathematics.\n\n",
|
1285 |
+
"target_delimiter": " ",
|
1286 |
+
"fewshot_delimiter": "\n\n",
|
1287 |
+
"fewshot_config": {
|
1288 |
+
"sampler": "first_n"
|
1289 |
+
},
|
1290 |
+
"metric_list": [
|
1291 |
+
{
|
1292 |
+
"metric": "acc",
|
1293 |
+
"aggregation": "mean",
|
1294 |
+
"higher_is_better": true
|
1295 |
+
}
|
1296 |
+
],
|
1297 |
+
"output_type": "multiple_choice",
|
1298 |
+
"repeats": 1,
|
1299 |
+
"should_decontaminate": false,
|
1300 |
+
"metadata": {
|
1301 |
+
"version": 0.0
|
1302 |
+
}
|
1303 |
+
},
|
1304 |
+
"mmlu_high_school_microeconomics": {
|
1305 |
+
"task": "mmlu_high_school_microeconomics",
|
1306 |
+
"task_alias": "high_school_microeconomics",
|
1307 |
+
"group": "mmlu_social_sciences",
|
1308 |
+
"group_alias": "social_sciences",
|
1309 |
+
"dataset_path": "hails/mmlu_no_train",
|
1310 |
+
"dataset_name": "high_school_microeconomics",
|
1311 |
+
"test_split": "test",
|
1312 |
+
"fewshot_split": "dev",
|
1313 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1314 |
+
"doc_to_target": "answer",
|
1315 |
+
"doc_to_choice": [
|
1316 |
+
"A",
|
1317 |
+
"B",
|
1318 |
+
"C",
|
1319 |
+
"D"
|
1320 |
+
],
|
1321 |
+
"description": "The following are multiple choice questions (with answers) about high school microeconomics.\n\n",
|
1322 |
+
"target_delimiter": " ",
|
1323 |
+
"fewshot_delimiter": "\n\n",
|
1324 |
+
"fewshot_config": {
|
1325 |
+
"sampler": "first_n"
|
1326 |
+
},
|
1327 |
+
"metric_list": [
|
1328 |
+
{
|
1329 |
+
"metric": "acc",
|
1330 |
+
"aggregation": "mean",
|
1331 |
+
"higher_is_better": true
|
1332 |
+
}
|
1333 |
+
],
|
1334 |
+
"output_type": "multiple_choice",
|
1335 |
+
"repeats": 1,
|
1336 |
+
"should_decontaminate": false,
|
1337 |
+
"metadata": {
|
1338 |
+
"version": 0.0
|
1339 |
+
}
|
1340 |
+
},
|
1341 |
+
"mmlu_high_school_physics": {
|
1342 |
+
"task": "mmlu_high_school_physics",
|
1343 |
+
"task_alias": "high_school_physics",
|
1344 |
+
"group": "mmlu_stem",
|
1345 |
+
"group_alias": "stem",
|
1346 |
+
"dataset_path": "hails/mmlu_no_train",
|
1347 |
+
"dataset_name": "high_school_physics",
|
1348 |
+
"test_split": "test",
|
1349 |
+
"fewshot_split": "dev",
|
1350 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1351 |
+
"doc_to_target": "answer",
|
1352 |
+
"doc_to_choice": [
|
1353 |
+
"A",
|
1354 |
+
"B",
|
1355 |
+
"C",
|
1356 |
+
"D"
|
1357 |
+
],
|
1358 |
+
"description": "The following are multiple choice questions (with answers) about high school physics.\n\n",
|
1359 |
+
"target_delimiter": " ",
|
1360 |
+
"fewshot_delimiter": "\n\n",
|
1361 |
+
"fewshot_config": {
|
1362 |
+
"sampler": "first_n"
|
1363 |
+
},
|
1364 |
+
"metric_list": [
|
1365 |
+
{
|
1366 |
+
"metric": "acc",
|
1367 |
+
"aggregation": "mean",
|
1368 |
+
"higher_is_better": true
|
1369 |
+
}
|
1370 |
+
],
|
1371 |
+
"output_type": "multiple_choice",
|
1372 |
+
"repeats": 1,
|
1373 |
+
"should_decontaminate": false,
|
1374 |
+
"metadata": {
|
1375 |
+
"version": 0.0
|
1376 |
+
}
|
1377 |
+
},
|
1378 |
+
"mmlu_high_school_psychology": {
|
1379 |
+
"task": "mmlu_high_school_psychology",
|
1380 |
+
"task_alias": "high_school_psychology",
|
1381 |
+
"group": "mmlu_social_sciences",
|
1382 |
+
"group_alias": "social_sciences",
|
1383 |
+
"dataset_path": "hails/mmlu_no_train",
|
1384 |
+
"dataset_name": "high_school_psychology",
|
1385 |
+
"test_split": "test",
|
1386 |
+
"fewshot_split": "dev",
|
1387 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1388 |
+
"doc_to_target": "answer",
|
1389 |
+
"doc_to_choice": [
|
1390 |
+
"A",
|
1391 |
+
"B",
|
1392 |
+
"C",
|
1393 |
+
"D"
|
1394 |
+
],
|
1395 |
+
"description": "The following are multiple choice questions (with answers) about high school psychology.\n\n",
|
1396 |
+
"target_delimiter": " ",
|
1397 |
+
"fewshot_delimiter": "\n\n",
|
1398 |
+
"fewshot_config": {
|
1399 |
+
"sampler": "first_n"
|
1400 |
+
},
|
1401 |
+
"metric_list": [
|
1402 |
+
{
|
1403 |
+
"metric": "acc",
|
1404 |
+
"aggregation": "mean",
|
1405 |
+
"higher_is_better": true
|
1406 |
+
}
|
1407 |
+
],
|
1408 |
+
"output_type": "multiple_choice",
|
1409 |
+
"repeats": 1,
|
1410 |
+
"should_decontaminate": false,
|
1411 |
+
"metadata": {
|
1412 |
+
"version": 0.0
|
1413 |
+
}
|
1414 |
+
},
|
1415 |
+
"mmlu_high_school_statistics": {
|
1416 |
+
"task": "mmlu_high_school_statistics",
|
1417 |
+
"task_alias": "high_school_statistics",
|
1418 |
+
"group": "mmlu_stem",
|
1419 |
+
"group_alias": "stem",
|
1420 |
+
"dataset_path": "hails/mmlu_no_train",
|
1421 |
+
"dataset_name": "high_school_statistics",
|
1422 |
+
"test_split": "test",
|
1423 |
+
"fewshot_split": "dev",
|
1424 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1425 |
+
"doc_to_target": "answer",
|
1426 |
+
"doc_to_choice": [
|
1427 |
+
"A",
|
1428 |
+
"B",
|
1429 |
+
"C",
|
1430 |
+
"D"
|
1431 |
+
],
|
1432 |
+
"description": "The following are multiple choice questions (with answers) about high school statistics.\n\n",
|
1433 |
+
"target_delimiter": " ",
|
1434 |
+
"fewshot_delimiter": "\n\n",
|
1435 |
+
"fewshot_config": {
|
1436 |
+
"sampler": "first_n"
|
1437 |
+
},
|
1438 |
+
"metric_list": [
|
1439 |
+
{
|
1440 |
+
"metric": "acc",
|
1441 |
+
"aggregation": "mean",
|
1442 |
+
"higher_is_better": true
|
1443 |
+
}
|
1444 |
+
],
|
1445 |
+
"output_type": "multiple_choice",
|
1446 |
+
"repeats": 1,
|
1447 |
+
"should_decontaminate": false,
|
1448 |
+
"metadata": {
|
1449 |
+
"version": 0.0
|
1450 |
+
}
|
1451 |
+
},
|
1452 |
+
"mmlu_high_school_us_history": {
|
1453 |
+
"task": "mmlu_high_school_us_history",
|
1454 |
+
"task_alias": "high_school_us_history",
|
1455 |
+
"group": "mmlu_humanities",
|
1456 |
+
"group_alias": "humanities",
|
1457 |
+
"dataset_path": "hails/mmlu_no_train",
|
1458 |
+
"dataset_name": "high_school_us_history",
|
1459 |
+
"test_split": "test",
|
1460 |
+
"fewshot_split": "dev",
|
1461 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1462 |
+
"doc_to_target": "answer",
|
1463 |
+
"doc_to_choice": [
|
1464 |
+
"A",
|
1465 |
+
"B",
|
1466 |
+
"C",
|
1467 |
+
"D"
|
1468 |
+
],
|
1469 |
+
"description": "The following are multiple choice questions (with answers) about high school us history.\n\n",
|
1470 |
+
"target_delimiter": " ",
|
1471 |
+
"fewshot_delimiter": "\n\n",
|
1472 |
+
"fewshot_config": {
|
1473 |
+
"sampler": "first_n"
|
1474 |
+
},
|
1475 |
+
"metric_list": [
|
1476 |
+
{
|
1477 |
+
"metric": "acc",
|
1478 |
+
"aggregation": "mean",
|
1479 |
+
"higher_is_better": true
|
1480 |
+
}
|
1481 |
+
],
|
1482 |
+
"output_type": "multiple_choice",
|
1483 |
+
"repeats": 1,
|
1484 |
+
"should_decontaminate": false,
|
1485 |
+
"metadata": {
|
1486 |
+
"version": 0.0
|
1487 |
+
}
|
1488 |
+
},
|
1489 |
+
"mmlu_high_school_world_history": {
|
1490 |
+
"task": "mmlu_high_school_world_history",
|
1491 |
+
"task_alias": "high_school_world_history",
|
1492 |
+
"group": "mmlu_humanities",
|
1493 |
+
"group_alias": "humanities",
|
1494 |
+
"dataset_path": "hails/mmlu_no_train",
|
1495 |
+
"dataset_name": "high_school_world_history",
|
1496 |
+
"test_split": "test",
|
1497 |
+
"fewshot_split": "dev",
|
1498 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1499 |
+
"doc_to_target": "answer",
|
1500 |
+
"doc_to_choice": [
|
1501 |
+
"A",
|
1502 |
+
"B",
|
1503 |
+
"C",
|
1504 |
+
"D"
|
1505 |
+
],
|
1506 |
+
"description": "The following are multiple choice questions (with answers) about high school world history.\n\n",
|
1507 |
+
"target_delimiter": " ",
|
1508 |
+
"fewshot_delimiter": "\n\n",
|
1509 |
+
"fewshot_config": {
|
1510 |
+
"sampler": "first_n"
|
1511 |
+
},
|
1512 |
+
"metric_list": [
|
1513 |
+
{
|
1514 |
+
"metric": "acc",
|
1515 |
+
"aggregation": "mean",
|
1516 |
+
"higher_is_better": true
|
1517 |
+
}
|
1518 |
+
],
|
1519 |
+
"output_type": "multiple_choice",
|
1520 |
+
"repeats": 1,
|
1521 |
+
"should_decontaminate": false,
|
1522 |
+
"metadata": {
|
1523 |
+
"version": 0.0
|
1524 |
+
}
|
1525 |
+
},
|
1526 |
+
"mmlu_human_aging": {
|
1527 |
+
"task": "mmlu_human_aging",
|
1528 |
+
"task_alias": "human_aging",
|
1529 |
+
"group": "mmlu_other",
|
1530 |
+
"group_alias": "other",
|
1531 |
+
"dataset_path": "hails/mmlu_no_train",
|
1532 |
+
"dataset_name": "human_aging",
|
1533 |
+
"test_split": "test",
|
1534 |
+
"fewshot_split": "dev",
|
1535 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1536 |
+
"doc_to_target": "answer",
|
1537 |
+
"doc_to_choice": [
|
1538 |
+
"A",
|
1539 |
+
"B",
|
1540 |
+
"C",
|
1541 |
+
"D"
|
1542 |
+
],
|
1543 |
+
"description": "The following are multiple choice questions (with answers) about human aging.\n\n",
|
1544 |
+
"target_delimiter": " ",
|
1545 |
+
"fewshot_delimiter": "\n\n",
|
1546 |
+
"fewshot_config": {
|
1547 |
+
"sampler": "first_n"
|
1548 |
+
},
|
1549 |
+
"metric_list": [
|
1550 |
+
{
|
1551 |
+
"metric": "acc",
|
1552 |
+
"aggregation": "mean",
|
1553 |
+
"higher_is_better": true
|
1554 |
+
}
|
1555 |
+
],
|
1556 |
+
"output_type": "multiple_choice",
|
1557 |
+
"repeats": 1,
|
1558 |
+
"should_decontaminate": false,
|
1559 |
+
"metadata": {
|
1560 |
+
"version": 0.0
|
1561 |
+
}
|
1562 |
+
},
|
1563 |
+
"mmlu_human_sexuality": {
|
1564 |
+
"task": "mmlu_human_sexuality",
|
1565 |
+
"task_alias": "human_sexuality",
|
1566 |
+
"group": "mmlu_social_sciences",
|
1567 |
+
"group_alias": "social_sciences",
|
1568 |
+
"dataset_path": "hails/mmlu_no_train",
|
1569 |
+
"dataset_name": "human_sexuality",
|
1570 |
+
"test_split": "test",
|
1571 |
+
"fewshot_split": "dev",
|
1572 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1573 |
+
"doc_to_target": "answer",
|
1574 |
+
"doc_to_choice": [
|
1575 |
+
"A",
|
1576 |
+
"B",
|
1577 |
+
"C",
|
1578 |
+
"D"
|
1579 |
+
],
|
1580 |
+
"description": "The following are multiple choice questions (with answers) about human sexuality.\n\n",
|
1581 |
+
"target_delimiter": " ",
|
1582 |
+
"fewshot_delimiter": "\n\n",
|
1583 |
+
"fewshot_config": {
|
1584 |
+
"sampler": "first_n"
|
1585 |
+
},
|
1586 |
+
"metric_list": [
|
1587 |
+
{
|
1588 |
+
"metric": "acc",
|
1589 |
+
"aggregation": "mean",
|
1590 |
+
"higher_is_better": true
|
1591 |
+
}
|
1592 |
+
],
|
1593 |
+
"output_type": "multiple_choice",
|
1594 |
+
"repeats": 1,
|
1595 |
+
"should_decontaminate": false,
|
1596 |
+
"metadata": {
|
1597 |
+
"version": 0.0
|
1598 |
+
}
|
1599 |
+
},
|
1600 |
+
"mmlu_international_law": {
|
1601 |
+
"task": "mmlu_international_law",
|
1602 |
+
"task_alias": "international_law",
|
1603 |
+
"group": "mmlu_humanities",
|
1604 |
+
"group_alias": "humanities",
|
1605 |
+
"dataset_path": "hails/mmlu_no_train",
|
1606 |
+
"dataset_name": "international_law",
|
1607 |
+
"test_split": "test",
|
1608 |
+
"fewshot_split": "dev",
|
1609 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1610 |
+
"doc_to_target": "answer",
|
1611 |
+
"doc_to_choice": [
|
1612 |
+
"A",
|
1613 |
+
"B",
|
1614 |
+
"C",
|
1615 |
+
"D"
|
1616 |
+
],
|
1617 |
+
"description": "The following are multiple choice questions (with answers) about international law.\n\n",
|
1618 |
+
"target_delimiter": " ",
|
1619 |
+
"fewshot_delimiter": "\n\n",
|
1620 |
+
"fewshot_config": {
|
1621 |
+
"sampler": "first_n"
|
1622 |
+
},
|
1623 |
+
"metric_list": [
|
1624 |
+
{
|
1625 |
+
"metric": "acc",
|
1626 |
+
"aggregation": "mean",
|
1627 |
+
"higher_is_better": true
|
1628 |
+
}
|
1629 |
+
],
|
1630 |
+
"output_type": "multiple_choice",
|
1631 |
+
"repeats": 1,
|
1632 |
+
"should_decontaminate": false,
|
1633 |
+
"metadata": {
|
1634 |
+
"version": 0.0
|
1635 |
+
}
|
1636 |
+
},
|
1637 |
+
"mmlu_jurisprudence": {
|
1638 |
+
"task": "mmlu_jurisprudence",
|
1639 |
+
"task_alias": "jurisprudence",
|
1640 |
+
"group": "mmlu_humanities",
|
1641 |
+
"group_alias": "humanities",
|
1642 |
+
"dataset_path": "hails/mmlu_no_train",
|
1643 |
+
"dataset_name": "jurisprudence",
|
1644 |
+
"test_split": "test",
|
1645 |
+
"fewshot_split": "dev",
|
1646 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1647 |
+
"doc_to_target": "answer",
|
1648 |
+
"doc_to_choice": [
|
1649 |
+
"A",
|
1650 |
+
"B",
|
1651 |
+
"C",
|
1652 |
+
"D"
|
1653 |
+
],
|
1654 |
+
"description": "The following are multiple choice questions (with answers) about jurisprudence.\n\n",
|
1655 |
+
"target_delimiter": " ",
|
1656 |
+
"fewshot_delimiter": "\n\n",
|
1657 |
+
"fewshot_config": {
|
1658 |
+
"sampler": "first_n"
|
1659 |
+
},
|
1660 |
+
"metric_list": [
|
1661 |
+
{
|
1662 |
+
"metric": "acc",
|
1663 |
+
"aggregation": "mean",
|
1664 |
+
"higher_is_better": true
|
1665 |
+
}
|
1666 |
+
],
|
1667 |
+
"output_type": "multiple_choice",
|
1668 |
+
"repeats": 1,
|
1669 |
+
"should_decontaminate": false,
|
1670 |
+
"metadata": {
|
1671 |
+
"version": 0.0
|
1672 |
+
}
|
1673 |
+
},
|
1674 |
+
"mmlu_logical_fallacies": {
|
1675 |
+
"task": "mmlu_logical_fallacies",
|
1676 |
+
"task_alias": "logical_fallacies",
|
1677 |
+
"group": "mmlu_humanities",
|
1678 |
+
"group_alias": "humanities",
|
1679 |
+
"dataset_path": "hails/mmlu_no_train",
|
1680 |
+
"dataset_name": "logical_fallacies",
|
1681 |
+
"test_split": "test",
|
1682 |
+
"fewshot_split": "dev",
|
1683 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1684 |
+
"doc_to_target": "answer",
|
1685 |
+
"doc_to_choice": [
|
1686 |
+
"A",
|
1687 |
+
"B",
|
1688 |
+
"C",
|
1689 |
+
"D"
|
1690 |
+
],
|
1691 |
+
"description": "The following are multiple choice questions (with answers) about logical fallacies.\n\n",
|
1692 |
+
"target_delimiter": " ",
|
1693 |
+
"fewshot_delimiter": "\n\n",
|
1694 |
+
"fewshot_config": {
|
1695 |
+
"sampler": "first_n"
|
1696 |
+
},
|
1697 |
+
"metric_list": [
|
1698 |
+
{
|
1699 |
+
"metric": "acc",
|
1700 |
+
"aggregation": "mean",
|
1701 |
+
"higher_is_better": true
|
1702 |
+
}
|
1703 |
+
],
|
1704 |
+
"output_type": "multiple_choice",
|
1705 |
+
"repeats": 1,
|
1706 |
+
"should_decontaminate": false,
|
1707 |
+
"metadata": {
|
1708 |
+
"version": 0.0
|
1709 |
+
}
|
1710 |
+
},
|
1711 |
+
"mmlu_machine_learning": {
|
1712 |
+
"task": "mmlu_machine_learning",
|
1713 |
+
"task_alias": "machine_learning",
|
1714 |
+
"group": "mmlu_stem",
|
1715 |
+
"group_alias": "stem",
|
1716 |
+
"dataset_path": "hails/mmlu_no_train",
|
1717 |
+
"dataset_name": "machine_learning",
|
1718 |
+
"test_split": "test",
|
1719 |
+
"fewshot_split": "dev",
|
1720 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1721 |
+
"doc_to_target": "answer",
|
1722 |
+
"doc_to_choice": [
|
1723 |
+
"A",
|
1724 |
+
"B",
|
1725 |
+
"C",
|
1726 |
+
"D"
|
1727 |
+
],
|
1728 |
+
"description": "The following are multiple choice questions (with answers) about machine learning.\n\n",
|
1729 |
+
"target_delimiter": " ",
|
1730 |
+
"fewshot_delimiter": "\n\n",
|
1731 |
+
"fewshot_config": {
|
1732 |
+
"sampler": "first_n"
|
1733 |
+
},
|
1734 |
+
"metric_list": [
|
1735 |
+
{
|
1736 |
+
"metric": "acc",
|
1737 |
+
"aggregation": "mean",
|
1738 |
+
"higher_is_better": true
|
1739 |
+
}
|
1740 |
+
],
|
1741 |
+
"output_type": "multiple_choice",
|
1742 |
+
"repeats": 1,
|
1743 |
+
"should_decontaminate": false,
|
1744 |
+
"metadata": {
|
1745 |
+
"version": 0.0
|
1746 |
+
}
|
1747 |
+
},
|
1748 |
+
"mmlu_management": {
|
1749 |
+
"task": "mmlu_management",
|
1750 |
+
"task_alias": "management",
|
1751 |
+
"group": "mmlu_other",
|
1752 |
+
"group_alias": "other",
|
1753 |
+
"dataset_path": "hails/mmlu_no_train",
|
1754 |
+
"dataset_name": "management",
|
1755 |
+
"test_split": "test",
|
1756 |
+
"fewshot_split": "dev",
|
1757 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1758 |
+
"doc_to_target": "answer",
|
1759 |
+
"doc_to_choice": [
|
1760 |
+
"A",
|
1761 |
+
"B",
|
1762 |
+
"C",
|
1763 |
+
"D"
|
1764 |
+
],
|
1765 |
+
"description": "The following are multiple choice questions (with answers) about management.\n\n",
|
1766 |
+
"target_delimiter": " ",
|
1767 |
+
"fewshot_delimiter": "\n\n",
|
1768 |
+
"fewshot_config": {
|
1769 |
+
"sampler": "first_n"
|
1770 |
+
},
|
1771 |
+
"metric_list": [
|
1772 |
+
{
|
1773 |
+
"metric": "acc",
|
1774 |
+
"aggregation": "mean",
|
1775 |
+
"higher_is_better": true
|
1776 |
+
}
|
1777 |
+
],
|
1778 |
+
"output_type": "multiple_choice",
|
1779 |
+
"repeats": 1,
|
1780 |
+
"should_decontaminate": false,
|
1781 |
+
"metadata": {
|
1782 |
+
"version": 0.0
|
1783 |
+
}
|
1784 |
+
},
|
1785 |
+
"mmlu_marketing": {
|
1786 |
+
"task": "mmlu_marketing",
|
1787 |
+
"task_alias": "marketing",
|
1788 |
+
"group": "mmlu_other",
|
1789 |
+
"group_alias": "other",
|
1790 |
+
"dataset_path": "hails/mmlu_no_train",
|
1791 |
+
"dataset_name": "marketing",
|
1792 |
+
"test_split": "test",
|
1793 |
+
"fewshot_split": "dev",
|
1794 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1795 |
+
"doc_to_target": "answer",
|
1796 |
+
"doc_to_choice": [
|
1797 |
+
"A",
|
1798 |
+
"B",
|
1799 |
+
"C",
|
1800 |
+
"D"
|
1801 |
+
],
|
1802 |
+
"description": "The following are multiple choice questions (with answers) about marketing.\n\n",
|
1803 |
+
"target_delimiter": " ",
|
1804 |
+
"fewshot_delimiter": "\n\n",
|
1805 |
+
"fewshot_config": {
|
1806 |
+
"sampler": "first_n"
|
1807 |
+
},
|
1808 |
+
"metric_list": [
|
1809 |
+
{
|
1810 |
+
"metric": "acc",
|
1811 |
+
"aggregation": "mean",
|
1812 |
+
"higher_is_better": true
|
1813 |
+
}
|
1814 |
+
],
|
1815 |
+
"output_type": "multiple_choice",
|
1816 |
+
"repeats": 1,
|
1817 |
+
"should_decontaminate": false,
|
1818 |
+
"metadata": {
|
1819 |
+
"version": 0.0
|
1820 |
+
}
|
1821 |
+
},
|
1822 |
+
"mmlu_medical_genetics": {
|
1823 |
+
"task": "mmlu_medical_genetics",
|
1824 |
+
"task_alias": "medical_genetics",
|
1825 |
+
"group": "mmlu_other",
|
1826 |
+
"group_alias": "other",
|
1827 |
+
"dataset_path": "hails/mmlu_no_train",
|
1828 |
+
"dataset_name": "medical_genetics",
|
1829 |
+
"test_split": "test",
|
1830 |
+
"fewshot_split": "dev",
|
1831 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1832 |
+
"doc_to_target": "answer",
|
1833 |
+
"doc_to_choice": [
|
1834 |
+
"A",
|
1835 |
+
"B",
|
1836 |
+
"C",
|
1837 |
+
"D"
|
1838 |
+
],
|
1839 |
+
"description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
|
1840 |
+
"target_delimiter": " ",
|
1841 |
+
"fewshot_delimiter": "\n\n",
|
1842 |
+
"fewshot_config": {
|
1843 |
+
"sampler": "first_n"
|
1844 |
+
},
|
1845 |
+
"metric_list": [
|
1846 |
+
{
|
1847 |
+
"metric": "acc",
|
1848 |
+
"aggregation": "mean",
|
1849 |
+
"higher_is_better": true
|
1850 |
+
}
|
1851 |
+
],
|
1852 |
+
"output_type": "multiple_choice",
|
1853 |
+
"repeats": 1,
|
1854 |
+
"should_decontaminate": false,
|
1855 |
+
"metadata": {
|
1856 |
+
"version": 0.0
|
1857 |
+
}
|
1858 |
+
},
|
1859 |
+
"mmlu_miscellaneous": {
|
1860 |
+
"task": "mmlu_miscellaneous",
|
1861 |
+
"task_alias": "miscellaneous",
|
1862 |
+
"group": "mmlu_other",
|
1863 |
+
"group_alias": "other",
|
1864 |
+
"dataset_path": "hails/mmlu_no_train",
|
1865 |
+
"dataset_name": "miscellaneous",
|
1866 |
+
"test_split": "test",
|
1867 |
+
"fewshot_split": "dev",
|
1868 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1869 |
+
"doc_to_target": "answer",
|
1870 |
+
"doc_to_choice": [
|
1871 |
+
"A",
|
1872 |
+
"B",
|
1873 |
+
"C",
|
1874 |
+
"D"
|
1875 |
+
],
|
1876 |
+
"description": "The following are multiple choice questions (with answers) about miscellaneous.\n\n",
|
1877 |
+
"target_delimiter": " ",
|
1878 |
+
"fewshot_delimiter": "\n\n",
|
1879 |
+
"fewshot_config": {
|
1880 |
+
"sampler": "first_n"
|
1881 |
+
},
|
1882 |
+
"metric_list": [
|
1883 |
+
{
|
1884 |
+
"metric": "acc",
|
1885 |
+
"aggregation": "mean",
|
1886 |
+
"higher_is_better": true
|
1887 |
+
}
|
1888 |
+
],
|
1889 |
+
"output_type": "multiple_choice",
|
1890 |
+
"repeats": 1,
|
1891 |
+
"should_decontaminate": false,
|
1892 |
+
"metadata": {
|
1893 |
+
"version": 0.0
|
1894 |
+
}
|
1895 |
+
},
|
1896 |
+
"mmlu_moral_disputes": {
|
1897 |
+
"task": "mmlu_moral_disputes",
|
1898 |
+
"task_alias": "moral_disputes",
|
1899 |
+
"group": "mmlu_humanities",
|
1900 |
+
"group_alias": "humanities",
|
1901 |
+
"dataset_path": "hails/mmlu_no_train",
|
1902 |
+
"dataset_name": "moral_disputes",
|
1903 |
+
"test_split": "test",
|
1904 |
+
"fewshot_split": "dev",
|
1905 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1906 |
+
"doc_to_target": "answer",
|
1907 |
+
"doc_to_choice": [
|
1908 |
+
"A",
|
1909 |
+
"B",
|
1910 |
+
"C",
|
1911 |
+
"D"
|
1912 |
+
],
|
1913 |
+
"description": "The following are multiple choice questions (with answers) about moral disputes.\n\n",
|
1914 |
+
"target_delimiter": " ",
|
1915 |
+
"fewshot_delimiter": "\n\n",
|
1916 |
+
"fewshot_config": {
|
1917 |
+
"sampler": "first_n"
|
1918 |
+
},
|
1919 |
+
"metric_list": [
|
1920 |
+
{
|
1921 |
+
"metric": "acc",
|
1922 |
+
"aggregation": "mean",
|
1923 |
+
"higher_is_better": true
|
1924 |
+
}
|
1925 |
+
],
|
1926 |
+
"output_type": "multiple_choice",
|
1927 |
+
"repeats": 1,
|
1928 |
+
"should_decontaminate": false,
|
1929 |
+
"metadata": {
|
1930 |
+
"version": 0.0
|
1931 |
+
}
|
1932 |
+
},
|
1933 |
+
"mmlu_moral_scenarios": {
|
1934 |
+
"task": "mmlu_moral_scenarios",
|
1935 |
+
"task_alias": "moral_scenarios",
|
1936 |
+
"group": "mmlu_humanities",
|
1937 |
+
"group_alias": "humanities",
|
1938 |
+
"dataset_path": "hails/mmlu_no_train",
|
1939 |
+
"dataset_name": "moral_scenarios",
|
1940 |
+
"test_split": "test",
|
1941 |
+
"fewshot_split": "dev",
|
1942 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1943 |
+
"doc_to_target": "answer",
|
1944 |
+
"doc_to_choice": [
|
1945 |
+
"A",
|
1946 |
+
"B",
|
1947 |
+
"C",
|
1948 |
+
"D"
|
1949 |
+
],
|
1950 |
+
"description": "The following are multiple choice questions (with answers) about moral scenarios.\n\n",
|
1951 |
+
"target_delimiter": " ",
|
1952 |
+
"fewshot_delimiter": "\n\n",
|
1953 |
+
"fewshot_config": {
|
1954 |
+
"sampler": "first_n"
|
1955 |
+
},
|
1956 |
+
"metric_list": [
|
1957 |
+
{
|
1958 |
+
"metric": "acc",
|
1959 |
+
"aggregation": "mean",
|
1960 |
+
"higher_is_better": true
|
1961 |
+
}
|
1962 |
+
],
|
1963 |
+
"output_type": "multiple_choice",
|
1964 |
+
"repeats": 1,
|
1965 |
+
"should_decontaminate": false,
|
1966 |
+
"metadata": {
|
1967 |
+
"version": 0.0
|
1968 |
+
}
|
1969 |
+
},
|
1970 |
+
"mmlu_nutrition": {
|
1971 |
+
"task": "mmlu_nutrition",
|
1972 |
+
"task_alias": "nutrition",
|
1973 |
+
"group": "mmlu_other",
|
1974 |
+
"group_alias": "other",
|
1975 |
+
"dataset_path": "hails/mmlu_no_train",
|
1976 |
+
"dataset_name": "nutrition",
|
1977 |
+
"test_split": "test",
|
1978 |
+
"fewshot_split": "dev",
|
1979 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1980 |
+
"doc_to_target": "answer",
|
1981 |
+
"doc_to_choice": [
|
1982 |
+
"A",
|
1983 |
+
"B",
|
1984 |
+
"C",
|
1985 |
+
"D"
|
1986 |
+
],
|
1987 |
+
"description": "The following are multiple choice questions (with answers) about nutrition.\n\n",
|
1988 |
+
"target_delimiter": " ",
|
1989 |
+
"fewshot_delimiter": "\n\n",
|
1990 |
+
"fewshot_config": {
|
1991 |
+
"sampler": "first_n"
|
1992 |
+
},
|
1993 |
+
"metric_list": [
|
1994 |
+
{
|
1995 |
+
"metric": "acc",
|
1996 |
+
"aggregation": "mean",
|
1997 |
+
"higher_is_better": true
|
1998 |
+
}
|
1999 |
+
],
|
2000 |
+
"output_type": "multiple_choice",
|
2001 |
+
"repeats": 1,
|
2002 |
+
"should_decontaminate": false,
|
2003 |
+
"metadata": {
|
2004 |
+
"version": 0.0
|
2005 |
+
}
|
2006 |
+
},
|
2007 |
+
"mmlu_philosophy": {
|
2008 |
+
"task": "mmlu_philosophy",
|
2009 |
+
"task_alias": "philosophy",
|
2010 |
+
"group": "mmlu_humanities",
|
2011 |
+
"group_alias": "humanities",
|
2012 |
+
"dataset_path": "hails/mmlu_no_train",
|
2013 |
+
"dataset_name": "philosophy",
|
2014 |
+
"test_split": "test",
|
2015 |
+
"fewshot_split": "dev",
|
2016 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2017 |
+
"doc_to_target": "answer",
|
2018 |
+
"doc_to_choice": [
|
2019 |
+
"A",
|
2020 |
+
"B",
|
2021 |
+
"C",
|
2022 |
+
"D"
|
2023 |
+
],
|
2024 |
+
"description": "The following are multiple choice questions (with answers) about philosophy.\n\n",
|
2025 |
+
"target_delimiter": " ",
|
2026 |
+
"fewshot_delimiter": "\n\n",
|
2027 |
+
"fewshot_config": {
|
2028 |
+
"sampler": "first_n"
|
2029 |
+
},
|
2030 |
+
"metric_list": [
|
2031 |
+
{
|
2032 |
+
"metric": "acc",
|
2033 |
+
"aggregation": "mean",
|
2034 |
+
"higher_is_better": true
|
2035 |
+
}
|
2036 |
+
],
|
2037 |
+
"output_type": "multiple_choice",
|
2038 |
+
"repeats": 1,
|
2039 |
+
"should_decontaminate": false,
|
2040 |
+
"metadata": {
|
2041 |
+
"version": 0.0
|
2042 |
+
}
|
2043 |
+
},
|
2044 |
+
"mmlu_prehistory": {
|
2045 |
+
"task": "mmlu_prehistory",
|
2046 |
+
"task_alias": "prehistory",
|
2047 |
+
"group": "mmlu_humanities",
|
2048 |
+
"group_alias": "humanities",
|
2049 |
+
"dataset_path": "hails/mmlu_no_train",
|
2050 |
+
"dataset_name": "prehistory",
|
2051 |
+
"test_split": "test",
|
2052 |
+
"fewshot_split": "dev",
|
2053 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2054 |
+
"doc_to_target": "answer",
|
2055 |
+
"doc_to_choice": [
|
2056 |
+
"A",
|
2057 |
+
"B",
|
2058 |
+
"C",
|
2059 |
+
"D"
|
2060 |
+
],
|
2061 |
+
"description": "The following are multiple choice questions (with answers) about prehistory.\n\n",
|
2062 |
+
"target_delimiter": " ",
|
2063 |
+
"fewshot_delimiter": "\n\n",
|
2064 |
+
"fewshot_config": {
|
2065 |
+
"sampler": "first_n"
|
2066 |
+
},
|
2067 |
+
"metric_list": [
|
2068 |
+
{
|
2069 |
+
"metric": "acc",
|
2070 |
+
"aggregation": "mean",
|
2071 |
+
"higher_is_better": true
|
2072 |
+
}
|
2073 |
+
],
|
2074 |
+
"output_type": "multiple_choice",
|
2075 |
+
"repeats": 1,
|
2076 |
+
"should_decontaminate": false,
|
2077 |
+
"metadata": {
|
2078 |
+
"version": 0.0
|
2079 |
+
}
|
2080 |
+
},
|
2081 |
+
"mmlu_professional_accounting": {
|
2082 |
+
"task": "mmlu_professional_accounting",
|
2083 |
+
"task_alias": "professional_accounting",
|
2084 |
+
"group": "mmlu_other",
|
2085 |
+
"group_alias": "other",
|
2086 |
+
"dataset_path": "hails/mmlu_no_train",
|
2087 |
+
"dataset_name": "professional_accounting",
|
2088 |
+
"test_split": "test",
|
2089 |
+
"fewshot_split": "dev",
|
2090 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2091 |
+
"doc_to_target": "answer",
|
2092 |
+
"doc_to_choice": [
|
2093 |
+
"A",
|
2094 |
+
"B",
|
2095 |
+
"C",
|
2096 |
+
"D"
|
2097 |
+
],
|
2098 |
+
"description": "The following are multiple choice questions (with answers) about professional accounting.\n\n",
|
2099 |
+
"target_delimiter": " ",
|
2100 |
+
"fewshot_delimiter": "\n\n",
|
2101 |
+
"fewshot_config": {
|
2102 |
+
"sampler": "first_n"
|
2103 |
+
},
|
2104 |
+
"metric_list": [
|
2105 |
+
{
|
2106 |
+
"metric": "acc",
|
2107 |
+
"aggregation": "mean",
|
2108 |
+
"higher_is_better": true
|
2109 |
+
}
|
2110 |
+
],
|
2111 |
+
"output_type": "multiple_choice",
|
2112 |
+
"repeats": 1,
|
2113 |
+
"should_decontaminate": false,
|
2114 |
+
"metadata": {
|
2115 |
+
"version": 0.0
|
2116 |
+
}
|
2117 |
+
},
|
2118 |
+
"mmlu_professional_law": {
|
2119 |
+
"task": "mmlu_professional_law",
|
2120 |
+
"task_alias": "professional_law",
|
2121 |
+
"group": "mmlu_humanities",
|
2122 |
+
"group_alias": "humanities",
|
2123 |
+
"dataset_path": "hails/mmlu_no_train",
|
2124 |
+
"dataset_name": "professional_law",
|
2125 |
+
"test_split": "test",
|
2126 |
+
"fewshot_split": "dev",
|
2127 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2128 |
+
"doc_to_target": "answer",
|
2129 |
+
"doc_to_choice": [
|
2130 |
+
"A",
|
2131 |
+
"B",
|
2132 |
+
"C",
|
2133 |
+
"D"
|
2134 |
+
],
|
2135 |
+
"description": "The following are multiple choice questions (with answers) about professional law.\n\n",
|
2136 |
+
"target_delimiter": " ",
|
2137 |
+
"fewshot_delimiter": "\n\n",
|
2138 |
+
"fewshot_config": {
|
2139 |
+
"sampler": "first_n"
|
2140 |
+
},
|
2141 |
+
"metric_list": [
|
2142 |
+
{
|
2143 |
+
"metric": "acc",
|
2144 |
+
"aggregation": "mean",
|
2145 |
+
"higher_is_better": true
|
2146 |
+
}
|
2147 |
+
],
|
2148 |
+
"output_type": "multiple_choice",
|
2149 |
+
"repeats": 1,
|
2150 |
+
"should_decontaminate": false,
|
2151 |
+
"metadata": {
|
2152 |
+
"version": 0.0
|
2153 |
+
}
|
2154 |
+
},
|
2155 |
+
"mmlu_professional_medicine": {
|
2156 |
+
"task": "mmlu_professional_medicine",
|
2157 |
+
"task_alias": "professional_medicine",
|
2158 |
+
"group": "mmlu_other",
|
2159 |
+
"group_alias": "other",
|
2160 |
+
"dataset_path": "hails/mmlu_no_train",
|
2161 |
+
"dataset_name": "professional_medicine",
|
2162 |
+
"test_split": "test",
|
2163 |
+
"fewshot_split": "dev",
|
2164 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2165 |
+
"doc_to_target": "answer",
|
2166 |
+
"doc_to_choice": [
|
2167 |
+
"A",
|
2168 |
+
"B",
|
2169 |
+
"C",
|
2170 |
+
"D"
|
2171 |
+
],
|
2172 |
+
"description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
|
2173 |
+
"target_delimiter": " ",
|
2174 |
+
"fewshot_delimiter": "\n\n",
|
2175 |
+
"fewshot_config": {
|
2176 |
+
"sampler": "first_n"
|
2177 |
+
},
|
2178 |
+
"metric_list": [
|
2179 |
+
{
|
2180 |
+
"metric": "acc",
|
2181 |
+
"aggregation": "mean",
|
2182 |
+
"higher_is_better": true
|
2183 |
+
}
|
2184 |
+
],
|
2185 |
+
"output_type": "multiple_choice",
|
2186 |
+
"repeats": 1,
|
2187 |
+
"should_decontaminate": false,
|
2188 |
+
"metadata": {
|
2189 |
+
"version": 0.0
|
2190 |
+
}
|
2191 |
+
},
|
2192 |
+
"mmlu_professional_psychology": {
|
2193 |
+
"task": "mmlu_professional_psychology",
|
2194 |
+
"task_alias": "professional_psychology",
|
2195 |
+
"group": "mmlu_social_sciences",
|
2196 |
+
"group_alias": "social_sciences",
|
2197 |
+
"dataset_path": "hails/mmlu_no_train",
|
2198 |
+
"dataset_name": "professional_psychology",
|
2199 |
+
"test_split": "test",
|
2200 |
+
"fewshot_split": "dev",
|
2201 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2202 |
+
"doc_to_target": "answer",
|
2203 |
+
"doc_to_choice": [
|
2204 |
+
"A",
|
2205 |
+
"B",
|
2206 |
+
"C",
|
2207 |
+
"D"
|
2208 |
+
],
|
2209 |
+
"description": "The following are multiple choice questions (with answers) about professional psychology.\n\n",
|
2210 |
+
"target_delimiter": " ",
|
2211 |
+
"fewshot_delimiter": "\n\n",
|
2212 |
+
"fewshot_config": {
|
2213 |
+
"sampler": "first_n"
|
2214 |
+
},
|
2215 |
+
"metric_list": [
|
2216 |
+
{
|
2217 |
+
"metric": "acc",
|
2218 |
+
"aggregation": "mean",
|
2219 |
+
"higher_is_better": true
|
2220 |
+
}
|
2221 |
+
],
|
2222 |
+
"output_type": "multiple_choice",
|
2223 |
+
"repeats": 1,
|
2224 |
+
"should_decontaminate": false,
|
2225 |
+
"metadata": {
|
2226 |
+
"version": 0.0
|
2227 |
+
}
|
2228 |
+
},
|
2229 |
+
"mmlu_public_relations": {
|
2230 |
+
"task": "mmlu_public_relations",
|
2231 |
+
"task_alias": "public_relations",
|
2232 |
+
"group": "mmlu_social_sciences",
|
2233 |
+
"group_alias": "social_sciences",
|
2234 |
+
"dataset_path": "hails/mmlu_no_train",
|
2235 |
+
"dataset_name": "public_relations",
|
2236 |
+
"test_split": "test",
|
2237 |
+
"fewshot_split": "dev",
|
2238 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2239 |
+
"doc_to_target": "answer",
|
2240 |
+
"doc_to_choice": [
|
2241 |
+
"A",
|
2242 |
+
"B",
|
2243 |
+
"C",
|
2244 |
+
"D"
|
2245 |
+
],
|
2246 |
+
"description": "The following are multiple choice questions (with answers) about public relations.\n\n",
|
2247 |
+
"target_delimiter": " ",
|
2248 |
+
"fewshot_delimiter": "\n\n",
|
2249 |
+
"fewshot_config": {
|
2250 |
+
"sampler": "first_n"
|
2251 |
+
},
|
2252 |
+
"metric_list": [
|
2253 |
+
{
|
2254 |
+
"metric": "acc",
|
2255 |
+
"aggregation": "mean",
|
2256 |
+
"higher_is_better": true
|
2257 |
+
}
|
2258 |
+
],
|
2259 |
+
"output_type": "multiple_choice",
|
2260 |
+
"repeats": 1,
|
2261 |
+
"should_decontaminate": false,
|
2262 |
+
"metadata": {
|
2263 |
+
"version": 0.0
|
2264 |
+
}
|
2265 |
+
},
|
2266 |
+
"mmlu_security_studies": {
|
2267 |
+
"task": "mmlu_security_studies",
|
2268 |
+
"task_alias": "security_studies",
|
2269 |
+
"group": "mmlu_social_sciences",
|
2270 |
+
"group_alias": "social_sciences",
|
2271 |
+
"dataset_path": "hails/mmlu_no_train",
|
2272 |
+
"dataset_name": "security_studies",
|
2273 |
+
"test_split": "test",
|
2274 |
+
"fewshot_split": "dev",
|
2275 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2276 |
+
"doc_to_target": "answer",
|
2277 |
+
"doc_to_choice": [
|
2278 |
+
"A",
|
2279 |
+
"B",
|
2280 |
+
"C",
|
2281 |
+
"D"
|
2282 |
+
],
|
2283 |
+
"description": "The following are multiple choice questions (with answers) about security studies.\n\n",
|
2284 |
+
"target_delimiter": " ",
|
2285 |
+
"fewshot_delimiter": "\n\n",
|
2286 |
+
"fewshot_config": {
|
2287 |
+
"sampler": "first_n"
|
2288 |
+
},
|
2289 |
+
"metric_list": [
|
2290 |
+
{
|
2291 |
+
"metric": "acc",
|
2292 |
+
"aggregation": "mean",
|
2293 |
+
"higher_is_better": true
|
2294 |
+
}
|
2295 |
+
],
|
2296 |
+
"output_type": "multiple_choice",
|
2297 |
+
"repeats": 1,
|
2298 |
+
"should_decontaminate": false,
|
2299 |
+
"metadata": {
|
2300 |
+
"version": 0.0
|
2301 |
+
}
|
2302 |
+
},
|
2303 |
+
"mmlu_sociology": {
|
2304 |
+
"task": "mmlu_sociology",
|
2305 |
+
"task_alias": "sociology",
|
2306 |
+
"group": "mmlu_social_sciences",
|
2307 |
+
"group_alias": "social_sciences",
|
2308 |
+
"dataset_path": "hails/mmlu_no_train",
|
2309 |
+
"dataset_name": "sociology",
|
2310 |
+
"test_split": "test",
|
2311 |
+
"fewshot_split": "dev",
|
2312 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2313 |
+
"doc_to_target": "answer",
|
2314 |
+
"doc_to_choice": [
|
2315 |
+
"A",
|
2316 |
+
"B",
|
2317 |
+
"C",
|
2318 |
+
"D"
|
2319 |
+
],
|
2320 |
+
"description": "The following are multiple choice questions (with answers) about sociology.\n\n",
|
2321 |
+
"target_delimiter": " ",
|
2322 |
+
"fewshot_delimiter": "\n\n",
|
2323 |
+
"fewshot_config": {
|
2324 |
+
"sampler": "first_n"
|
2325 |
+
},
|
2326 |
+
"metric_list": [
|
2327 |
+
{
|
2328 |
+
"metric": "acc",
|
2329 |
+
"aggregation": "mean",
|
2330 |
+
"higher_is_better": true
|
2331 |
+
}
|
2332 |
+
],
|
2333 |
+
"output_type": "multiple_choice",
|
2334 |
+
"repeats": 1,
|
2335 |
+
"should_decontaminate": false,
|
2336 |
+
"metadata": {
|
2337 |
+
"version": 0.0
|
2338 |
+
}
|
2339 |
+
},
|
2340 |
+
"mmlu_us_foreign_policy": {
|
2341 |
+
"task": "mmlu_us_foreign_policy",
|
2342 |
+
"task_alias": "us_foreign_policy",
|
2343 |
+
"group": "mmlu_social_sciences",
|
2344 |
+
"group_alias": "social_sciences",
|
2345 |
+
"dataset_path": "hails/mmlu_no_train",
|
2346 |
+
"dataset_name": "us_foreign_policy",
|
2347 |
+
"test_split": "test",
|
2348 |
+
"fewshot_split": "dev",
|
2349 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2350 |
+
"doc_to_target": "answer",
|
2351 |
+
"doc_to_choice": [
|
2352 |
+
"A",
|
2353 |
+
"B",
|
2354 |
+
"C",
|
2355 |
+
"D"
|
2356 |
+
],
|
2357 |
+
"description": "The following are multiple choice questions (with answers) about us foreign policy.\n\n",
|
2358 |
+
"target_delimiter": " ",
|
2359 |
+
"fewshot_delimiter": "\n\n",
|
2360 |
+
"fewshot_config": {
|
2361 |
+
"sampler": "first_n"
|
2362 |
+
},
|
2363 |
+
"metric_list": [
|
2364 |
+
{
|
2365 |
+
"metric": "acc",
|
2366 |
+
"aggregation": "mean",
|
2367 |
+
"higher_is_better": true
|
2368 |
+
}
|
2369 |
+
],
|
2370 |
+
"output_type": "multiple_choice",
|
2371 |
+
"repeats": 1,
|
2372 |
+
"should_decontaminate": false,
|
2373 |
+
"metadata": {
|
2374 |
+
"version": 0.0
|
2375 |
+
}
|
2376 |
+
},
|
2377 |
+
"mmlu_virology": {
|
2378 |
+
"task": "mmlu_virology",
|
2379 |
+
"task_alias": "virology",
|
2380 |
+
"group": "mmlu_other",
|
2381 |
+
"group_alias": "other",
|
2382 |
+
"dataset_path": "hails/mmlu_no_train",
|
2383 |
+
"dataset_name": "virology",
|
2384 |
+
"test_split": "test",
|
2385 |
+
"fewshot_split": "dev",
|
2386 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2387 |
+
"doc_to_target": "answer",
|
2388 |
+
"doc_to_choice": [
|
2389 |
+
"A",
|
2390 |
+
"B",
|
2391 |
+
"C",
|
2392 |
+
"D"
|
2393 |
+
],
|
2394 |
+
"description": "The following are multiple choice questions (with answers) about virology.\n\n",
|
2395 |
+
"target_delimiter": " ",
|
2396 |
+
"fewshot_delimiter": "\n\n",
|
2397 |
+
"fewshot_config": {
|
2398 |
+
"sampler": "first_n"
|
2399 |
+
},
|
2400 |
+
"metric_list": [
|
2401 |
+
{
|
2402 |
+
"metric": "acc",
|
2403 |
+
"aggregation": "mean",
|
2404 |
+
"higher_is_better": true
|
2405 |
+
}
|
2406 |
+
],
|
2407 |
+
"output_type": "multiple_choice",
|
2408 |
+
"repeats": 1,
|
2409 |
+
"should_decontaminate": false,
|
2410 |
+
"metadata": {
|
2411 |
+
"version": 0.0
|
2412 |
+
}
|
2413 |
+
},
|
2414 |
+
"mmlu_world_religions": {
|
2415 |
+
"task": "mmlu_world_religions",
|
2416 |
+
"task_alias": "world_religions",
|
2417 |
+
"group": "mmlu_humanities",
|
2418 |
+
"group_alias": "humanities",
|
2419 |
+
"dataset_path": "hails/mmlu_no_train",
|
2420 |
+
"dataset_name": "world_religions",
|
2421 |
+
"test_split": "test",
|
2422 |
+
"fewshot_split": "dev",
|
2423 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2424 |
+
"doc_to_target": "answer",
|
2425 |
+
"doc_to_choice": [
|
2426 |
+
"A",
|
2427 |
+
"B",
|
2428 |
+
"C",
|
2429 |
+
"D"
|
2430 |
+
],
|
2431 |
+
"description": "The following are multiple choice questions (with answers) about world religions.\n\n",
|
2432 |
+
"target_delimiter": " ",
|
2433 |
+
"fewshot_delimiter": "\n\n",
|
2434 |
+
"fewshot_config": {
|
2435 |
+
"sampler": "first_n"
|
2436 |
+
},
|
2437 |
+
"metric_list": [
|
2438 |
+
{
|
2439 |
+
"metric": "acc",
|
2440 |
+
"aggregation": "mean",
|
2441 |
+
"higher_is_better": true
|
2442 |
+
}
|
2443 |
+
],
|
2444 |
+
"output_type": "multiple_choice",
|
2445 |
+
"repeats": 1,
|
2446 |
+
"should_decontaminate": false,
|
2447 |
+
"metadata": {
|
2448 |
+
"version": 0.0
|
2449 |
+
}
|
2450 |
+
}
|
2451 |
+
},
|
2452 |
+
"versions": {
|
2453 |
+
"mmlu": "N/A",
|
2454 |
+
"mmlu_abstract_algebra": 0.0,
|
2455 |
+
"mmlu_anatomy": 0.0,
|
2456 |
+
"mmlu_astronomy": 0.0,
|
2457 |
+
"mmlu_business_ethics": 0.0,
|
2458 |
+
"mmlu_clinical_knowledge": 0.0,
|
2459 |
+
"mmlu_college_biology": 0.0,
|
2460 |
+
"mmlu_college_chemistry": 0.0,
|
2461 |
+
"mmlu_college_computer_science": 0.0,
|
2462 |
+
"mmlu_college_mathematics": 0.0,
|
2463 |
+
"mmlu_college_medicine": 0.0,
|
2464 |
+
"mmlu_college_physics": 0.0,
|
2465 |
+
"mmlu_computer_security": 0.0,
|
2466 |
+
"mmlu_conceptual_physics": 0.0,
|
2467 |
+
"mmlu_econometrics": 0.0,
|
2468 |
+
"mmlu_electrical_engineering": 0.0,
|
2469 |
+
"mmlu_elementary_mathematics": 0.0,
|
2470 |
+
"mmlu_formal_logic": 0.0,
|
2471 |
+
"mmlu_global_facts": 0.0,
|
2472 |
+
"mmlu_high_school_biology": 0.0,
|
2473 |
+
"mmlu_high_school_chemistry": 0.0,
|
2474 |
+
"mmlu_high_school_computer_science": 0.0,
|
2475 |
+
"mmlu_high_school_european_history": 0.0,
|
2476 |
+
"mmlu_high_school_geography": 0.0,
|
2477 |
+
"mmlu_high_school_government_and_politics": 0.0,
|
2478 |
+
"mmlu_high_school_macroeconomics": 0.0,
|
2479 |
+
"mmlu_high_school_mathematics": 0.0,
|
2480 |
+
"mmlu_high_school_microeconomics": 0.0,
|
2481 |
+
"mmlu_high_school_physics": 0.0,
|
2482 |
+
"mmlu_high_school_psychology": 0.0,
|
2483 |
+
"mmlu_high_school_statistics": 0.0,
|
2484 |
+
"mmlu_high_school_us_history": 0.0,
|
2485 |
+
"mmlu_high_school_world_history": 0.0,
|
2486 |
+
"mmlu_human_aging": 0.0,
|
2487 |
+
"mmlu_human_sexuality": 0.0,
|
2488 |
+
"mmlu_humanities": "N/A",
|
2489 |
+
"mmlu_international_law": 0.0,
|
2490 |
+
"mmlu_jurisprudence": 0.0,
|
2491 |
+
"mmlu_logical_fallacies": 0.0,
|
2492 |
+
"mmlu_machine_learning": 0.0,
|
2493 |
+
"mmlu_management": 0.0,
|
2494 |
+
"mmlu_marketing": 0.0,
|
2495 |
+
"mmlu_medical_genetics": 0.0,
|
2496 |
+
"mmlu_miscellaneous": 0.0,
|
2497 |
+
"mmlu_moral_disputes": 0.0,
|
2498 |
+
"mmlu_moral_scenarios": 0.0,
|
2499 |
+
"mmlu_nutrition": 0.0,
|
2500 |
+
"mmlu_other": "N/A",
|
2501 |
+
"mmlu_philosophy": 0.0,
|
2502 |
+
"mmlu_prehistory": 0.0,
|
2503 |
+
"mmlu_professional_accounting": 0.0,
|
2504 |
+
"mmlu_professional_law": 0.0,
|
2505 |
+
"mmlu_professional_medicine": 0.0,
|
2506 |
+
"mmlu_professional_psychology": 0.0,
|
2507 |
+
"mmlu_public_relations": 0.0,
|
2508 |
+
"mmlu_security_studies": 0.0,
|
2509 |
+
"mmlu_social_sciences": "N/A",
|
2510 |
+
"mmlu_sociology": 0.0,
|
2511 |
+
"mmlu_stem": "N/A",
|
2512 |
+
"mmlu_us_foreign_policy": 0.0,
|
2513 |
+
"mmlu_virology": 0.0,
|
2514 |
+
"mmlu_world_religions": 0.0
|
2515 |
+
},
|
2516 |
+
"n-shot": {
|
2517 |
+
"mmlu": 0,
|
2518 |
+
"mmlu_abstract_algebra": 0,
|
2519 |
+
"mmlu_anatomy": 0,
|
2520 |
+
"mmlu_astronomy": 0,
|
2521 |
+
"mmlu_business_ethics": 0,
|
2522 |
+
"mmlu_clinical_knowledge": 0,
|
2523 |
+
"mmlu_college_biology": 0,
|
2524 |
+
"mmlu_college_chemistry": 0,
|
2525 |
+
"mmlu_college_computer_science": 0,
|
2526 |
+
"mmlu_college_mathematics": 0,
|
2527 |
+
"mmlu_college_medicine": 0,
|
2528 |
+
"mmlu_college_physics": 0,
|
2529 |
+
"mmlu_computer_security": 0,
|
2530 |
+
"mmlu_conceptual_physics": 0,
|
2531 |
+
"mmlu_econometrics": 0,
|
2532 |
+
"mmlu_electrical_engineering": 0,
|
2533 |
+
"mmlu_elementary_mathematics": 0,
|
2534 |
+
"mmlu_formal_logic": 0,
|
2535 |
+
"mmlu_global_facts": 0,
|
2536 |
+
"mmlu_high_school_biology": 0,
|
2537 |
+
"mmlu_high_school_chemistry": 0,
|
2538 |
+
"mmlu_high_school_computer_science": 0,
|
2539 |
+
"mmlu_high_school_european_history": 0,
|
2540 |
+
"mmlu_high_school_geography": 0,
|
2541 |
+
"mmlu_high_school_government_and_politics": 0,
|
2542 |
+
"mmlu_high_school_macroeconomics": 0,
|
2543 |
+
"mmlu_high_school_mathematics": 0,
|
2544 |
+
"mmlu_high_school_microeconomics": 0,
|
2545 |
+
"mmlu_high_school_physics": 0,
|
2546 |
+
"mmlu_high_school_psychology": 0,
|
2547 |
+
"mmlu_high_school_statistics": 0,
|
2548 |
+
"mmlu_high_school_us_history": 0,
|
2549 |
+
"mmlu_high_school_world_history": 0,
|
2550 |
+
"mmlu_human_aging": 0,
|
2551 |
+
"mmlu_human_sexuality": 0,
|
2552 |
+
"mmlu_humanities": 0,
|
2553 |
+
"mmlu_international_law": 0,
|
2554 |
+
"mmlu_jurisprudence": 0,
|
2555 |
+
"mmlu_logical_fallacies": 0,
|
2556 |
+
"mmlu_machine_learning": 0,
|
2557 |
+
"mmlu_management": 0,
|
2558 |
+
"mmlu_marketing": 0,
|
2559 |
+
"mmlu_medical_genetics": 0,
|
2560 |
+
"mmlu_miscellaneous": 0,
|
2561 |
+
"mmlu_moral_disputes": 0,
|
2562 |
+
"mmlu_moral_scenarios": 0,
|
2563 |
+
"mmlu_nutrition": 0,
|
2564 |
+
"mmlu_other": 0,
|
2565 |
+
"mmlu_philosophy": 0,
|
2566 |
+
"mmlu_prehistory": 0,
|
2567 |
+
"mmlu_professional_accounting": 0,
|
2568 |
+
"mmlu_professional_law": 0,
|
2569 |
+
"mmlu_professional_medicine": 0,
|
2570 |
+
"mmlu_professional_psychology": 0,
|
2571 |
+
"mmlu_public_relations": 0,
|
2572 |
+
"mmlu_security_studies": 0,
|
2573 |
+
"mmlu_social_sciences": 0,
|
2574 |
+
"mmlu_sociology": 0,
|
2575 |
+
"mmlu_stem": 0,
|
2576 |
+
"mmlu_us_foreign_policy": 0,
|
2577 |
+
"mmlu_virology": 0,
|
2578 |
+
"mmlu_world_religions": 0
|
2579 |
+
},
|
2580 |
+
"config": {
|
2581 |
+
"model": "hf",
|
2582 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
2583 |
+
"batch_size": "auto",
|
2584 |
+
"batch_sizes": [
|
2585 |
+
32
|
2586 |
+
],
|
2587 |
+
"device": null,
|
2588 |
+
"use_cache": null,
|
2589 |
+
"limit": null,
|
2590 |
+
"bootstrap_iters": 100000,
|
2591 |
+
"gen_kwargs": null
|
2592 |
+
},
|
2593 |
+
"git_hash": "1ee41f7"
|
2594 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:287411341235a46519e378cb226b7d278648aa9c1ae2f258b4fc3ed273fbfe05
|
3 |
+
size 122208
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"openbookqa": {
|
4 |
+
"acc,none": 0.254,
|
5 |
+
"acc_stderr,none": 0.01948659680164338,
|
6 |
+
"acc_norm,none": 0.36,
|
7 |
+
"acc_norm_stderr,none": 0.021487751089720522,
|
8 |
+
"alias": "openbookqa"
|
9 |
+
}
|
10 |
+
},
|
11 |
+
"configs": {
|
12 |
+
"openbookqa": {
|
13 |
+
"task": "openbookqa",
|
14 |
+
"dataset_path": "openbookqa",
|
15 |
+
"dataset_name": "main",
|
16 |
+
"training_split": "train",
|
17 |
+
"validation_split": "validation",
|
18 |
+
"test_split": "test",
|
19 |
+
"doc_to_text": "question_stem",
|
20 |
+
"doc_to_target": "{{choices.label.index(answerKey.lstrip())}}",
|
21 |
+
"doc_to_choice": "{{choices.text}}",
|
22 |
+
"description": "",
|
23 |
+
"target_delimiter": " ",
|
24 |
+
"fewshot_delimiter": "\n\n",
|
25 |
+
"metric_list": [
|
26 |
+
{
|
27 |
+
"metric": "acc",
|
28 |
+
"aggregation": "mean",
|
29 |
+
"higher_is_better": true
|
30 |
+
},
|
31 |
+
{
|
32 |
+
"metric": "acc_norm",
|
33 |
+
"aggregation": "mean",
|
34 |
+
"higher_is_better": true
|
35 |
+
}
|
36 |
+
],
|
37 |
+
"output_type": "multiple_choice",
|
38 |
+
"repeats": 1,
|
39 |
+
"should_decontaminate": true,
|
40 |
+
"doc_to_decontamination_query": "question_stem",
|
41 |
+
"metadata": {
|
42 |
+
"version": 1.0
|
43 |
+
}
|
44 |
+
}
|
45 |
+
},
|
46 |
+
"versions": {
|
47 |
+
"openbookqa": 1.0
|
48 |
+
},
|
49 |
+
"n-shot": {
|
50 |
+
"openbookqa": 0
|
51 |
+
},
|
52 |
+
"config": {
|
53 |
+
"model": "hf",
|
54 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
55 |
+
"batch_size": "auto",
|
56 |
+
"batch_sizes": [
|
57 |
+
64
|
58 |
+
],
|
59 |
+
"device": null,
|
60 |
+
"use_cache": null,
|
61 |
+
"limit": null,
|
62 |
+
"bootstrap_iters": 100000,
|
63 |
+
"gen_kwargs": null
|
64 |
+
},
|
65 |
+
"git_hash": "1ee41f7"
|
66 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1dc0559bb19333d7600ca5fd2d783ae4a475351c97f1ceb5639c9e239f890941
|
3 |
+
size 36955
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,283 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"pawsx": {
|
4 |
+
"acc,none": 0.5192857142857144,
|
5 |
+
"acc_stderr,none": 0.029939594331147804,
|
6 |
+
"alias": "pawsx"
|
7 |
+
},
|
8 |
+
"paws_de": {
|
9 |
+
"acc,none": 0.4845,
|
10 |
+
"acc_stderr,none": 0.011177761232603322,
|
11 |
+
"alias": " - paws_de"
|
12 |
+
},
|
13 |
+
"paws_en": {
|
14 |
+
"acc,none": 0.456,
|
15 |
+
"acc_stderr,none": 0.011139750761283311,
|
16 |
+
"alias": " - paws_en"
|
17 |
+
},
|
18 |
+
"paws_es": {
|
19 |
+
"acc,none": 0.533,
|
20 |
+
"acc_stderr,none": 0.011158752568250675,
|
21 |
+
"alias": " - paws_es"
|
22 |
+
},
|
23 |
+
"paws_fr": {
|
24 |
+
"acc,none": 0.5485,
|
25 |
+
"acc_stderr,none": 0.011130400617630765,
|
26 |
+
"alias": " - paws_fr"
|
27 |
+
},
|
28 |
+
"paws_ja": {
|
29 |
+
"acc,none": 0.557,
|
30 |
+
"acc_stderr,none": 0.011110230358066709,
|
31 |
+
"alias": " - paws_ja"
|
32 |
+
},
|
33 |
+
"paws_ko": {
|
34 |
+
"acc,none": 0.52,
|
35 |
+
"acc_stderr,none": 0.011174185930778305,
|
36 |
+
"alias": " - paws_ko"
|
37 |
+
},
|
38 |
+
"paws_zh": {
|
39 |
+
"acc,none": 0.536,
|
40 |
+
"acc_stderr,none": 0.011154111668060216,
|
41 |
+
"alias": " - paws_zh"
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"groups": {
|
45 |
+
"pawsx": {
|
46 |
+
"acc,none": 0.5192857142857144,
|
47 |
+
"acc_stderr,none": 0.029939594331147804,
|
48 |
+
"alias": "pawsx"
|
49 |
+
}
|
50 |
+
},
|
51 |
+
"configs": {
|
52 |
+
"paws_de": {
|
53 |
+
"task": "paws_de",
|
54 |
+
"group": "pawsx",
|
55 |
+
"dataset_path": "paws-x",
|
56 |
+
"dataset_name": "de",
|
57 |
+
"training_split": "train",
|
58 |
+
"validation_split": "validation",
|
59 |
+
"test_split": "test",
|
60 |
+
"doc_to_text": "",
|
61 |
+
"doc_to_target": "label",
|
62 |
+
"doc_to_choice": "{{[sentence1+\", richtig? Ja, \"+sentence2, sentence1+\", richtig? Nein, \"+sentence2]}}",
|
63 |
+
"description": "",
|
64 |
+
"target_delimiter": " ",
|
65 |
+
"fewshot_delimiter": "\n\n",
|
66 |
+
"metric_list": [
|
67 |
+
{
|
68 |
+
"metric": "acc",
|
69 |
+
"aggregation": "mean",
|
70 |
+
"higher_is_better": true
|
71 |
+
}
|
72 |
+
],
|
73 |
+
"output_type": "multiple_choice",
|
74 |
+
"repeats": 1,
|
75 |
+
"should_decontaminate": false,
|
76 |
+
"metadata": {
|
77 |
+
"version": 0.0
|
78 |
+
}
|
79 |
+
},
|
80 |
+
"paws_en": {
|
81 |
+
"task": "paws_en",
|
82 |
+
"group": "pawsx",
|
83 |
+
"dataset_path": "paws-x",
|
84 |
+
"dataset_name": "en",
|
85 |
+
"training_split": "train",
|
86 |
+
"validation_split": "validation",
|
87 |
+
"test_split": "test",
|
88 |
+
"doc_to_text": "",
|
89 |
+
"doc_to_target": "label",
|
90 |
+
"doc_to_choice": "{{[sentence1+\", right? Yes, \"+sentence2, sentence1+\", right? No, \"+sentence2]}}",
|
91 |
+
"description": "",
|
92 |
+
"target_delimiter": " ",
|
93 |
+
"fewshot_delimiter": "\n\n",
|
94 |
+
"metric_list": [
|
95 |
+
{
|
96 |
+
"metric": "acc",
|
97 |
+
"aggregation": "mean",
|
98 |
+
"higher_is_better": true
|
99 |
+
}
|
100 |
+
],
|
101 |
+
"output_type": "multiple_choice",
|
102 |
+
"repeats": 1,
|
103 |
+
"should_decontaminate": false,
|
104 |
+
"metadata": {
|
105 |
+
"version": 0.0
|
106 |
+
}
|
107 |
+
},
|
108 |
+
"paws_es": {
|
109 |
+
"task": "paws_es",
|
110 |
+
"group": "pawsx",
|
111 |
+
"dataset_path": "paws-x",
|
112 |
+
"dataset_name": "es",
|
113 |
+
"training_split": "train",
|
114 |
+
"validation_split": "validation",
|
115 |
+
"test_split": "test",
|
116 |
+
"doc_to_text": "",
|
117 |
+
"doc_to_target": "label",
|
118 |
+
"doc_to_choice": "{{[sentence1+\", verdad? Sí, \"+sentence2, sentence1+\", verdad? No, \"+sentence2]}}",
|
119 |
+
"description": "",
|
120 |
+
"target_delimiter": " ",
|
121 |
+
"fewshot_delimiter": "\n\n",
|
122 |
+
"metric_list": [
|
123 |
+
{
|
124 |
+
"metric": "acc",
|
125 |
+
"aggregation": "mean",
|
126 |
+
"higher_is_better": true
|
127 |
+
}
|
128 |
+
],
|
129 |
+
"output_type": "multiple_choice",
|
130 |
+
"repeats": 1,
|
131 |
+
"should_decontaminate": false,
|
132 |
+
"metadata": {
|
133 |
+
"version": 0.0
|
134 |
+
}
|
135 |
+
},
|
136 |
+
"paws_fr": {
|
137 |
+
"task": "paws_fr",
|
138 |
+
"group": "pawsx",
|
139 |
+
"dataset_path": "paws-x",
|
140 |
+
"dataset_name": "fr",
|
141 |
+
"training_split": "train",
|
142 |
+
"validation_split": "validation",
|
143 |
+
"test_split": "test",
|
144 |
+
"doc_to_text": "",
|
145 |
+
"doc_to_target": "label",
|
146 |
+
"doc_to_choice": "{{[sentence1+\", n'est-ce pas? Oui, \"+sentence2, sentence1+\", n'est-ce pas? No, \"+sentence2]}}",
|
147 |
+
"description": "",
|
148 |
+
"target_delimiter": " ",
|
149 |
+
"fewshot_delimiter": "\n\n",
|
150 |
+
"metric_list": [
|
151 |
+
{
|
152 |
+
"metric": "acc",
|
153 |
+
"aggregation": "mean",
|
154 |
+
"higher_is_better": true
|
155 |
+
}
|
156 |
+
],
|
157 |
+
"output_type": "multiple_choice",
|
158 |
+
"repeats": 1,
|
159 |
+
"should_decontaminate": false,
|
160 |
+
"metadata": {
|
161 |
+
"version": 0.0
|
162 |
+
}
|
163 |
+
},
|
164 |
+
"paws_ja": {
|
165 |
+
"task": "paws_ja",
|
166 |
+
"group": "pawsx",
|
167 |
+
"dataset_path": "paws-x",
|
168 |
+
"dataset_name": "ja",
|
169 |
+
"training_split": "train",
|
170 |
+
"validation_split": "validation",
|
171 |
+
"test_split": "test",
|
172 |
+
"doc_to_text": "",
|
173 |
+
"doc_to_target": "label",
|
174 |
+
"doc_to_choice": "{{[sentence1+\", ですね? はい, \"+sentence2, sentence1+\", ですね? いいえ, \"+sentence2]}}",
|
175 |
+
"description": "",
|
176 |
+
"target_delimiter": " ",
|
177 |
+
"fewshot_delimiter": "\n\n",
|
178 |
+
"metric_list": [
|
179 |
+
{
|
180 |
+
"metric": "acc",
|
181 |
+
"aggregation": "mean",
|
182 |
+
"higher_is_better": true
|
183 |
+
}
|
184 |
+
],
|
185 |
+
"output_type": "multiple_choice",
|
186 |
+
"repeats": 1,
|
187 |
+
"should_decontaminate": false,
|
188 |
+
"metadata": {
|
189 |
+
"version": 0.0
|
190 |
+
}
|
191 |
+
},
|
192 |
+
"paws_ko": {
|
193 |
+
"task": "paws_ko",
|
194 |
+
"group": "pawsx",
|
195 |
+
"dataset_path": "paws-x",
|
196 |
+
"dataset_name": "ko",
|
197 |
+
"training_split": "train",
|
198 |
+
"validation_split": "validation",
|
199 |
+
"test_split": "test",
|
200 |
+
"doc_to_text": "",
|
201 |
+
"doc_to_target": "label",
|
202 |
+
"doc_to_choice": "{{[sentence1+\", 맞죠? 예, \"+sentence2, sentence1+\", 맞죠? 아니요, \"+sentence2]}}",
|
203 |
+
"description": "",
|
204 |
+
"target_delimiter": " ",
|
205 |
+
"fewshot_delimiter": "\n\n",
|
206 |
+
"metric_list": [
|
207 |
+
{
|
208 |
+
"metric": "acc",
|
209 |
+
"aggregation": "mean",
|
210 |
+
"higher_is_better": true
|
211 |
+
}
|
212 |
+
],
|
213 |
+
"output_type": "multiple_choice",
|
214 |
+
"repeats": 1,
|
215 |
+
"should_decontaminate": false,
|
216 |
+
"metadata": {
|
217 |
+
"version": 0.0
|
218 |
+
}
|
219 |
+
},
|
220 |
+
"paws_zh": {
|
221 |
+
"task": "paws_zh",
|
222 |
+
"group": "pawsx",
|
223 |
+
"dataset_path": "paws-x",
|
224 |
+
"dataset_name": "zh",
|
225 |
+
"training_split": "train",
|
226 |
+
"validation_split": "validation",
|
227 |
+
"test_split": "test",
|
228 |
+
"doc_to_text": "",
|
229 |
+
"doc_to_target": "label",
|
230 |
+
"doc_to_choice": "{{[sentence1+\", 对吧? 是, \"+sentence2, sentence1+\", 对吧? 不是, \"+sentence2]}}",
|
231 |
+
"description": "",
|
232 |
+
"target_delimiter": " ",
|
233 |
+
"fewshot_delimiter": "\n\n",
|
234 |
+
"metric_list": [
|
235 |
+
{
|
236 |
+
"metric": "acc",
|
237 |
+
"aggregation": "mean",
|
238 |
+
"higher_is_better": true
|
239 |
+
}
|
240 |
+
],
|
241 |
+
"output_type": "multiple_choice",
|
242 |
+
"repeats": 1,
|
243 |
+
"should_decontaminate": false,
|
244 |
+
"metadata": {
|
245 |
+
"version": 0.0
|
246 |
+
}
|
247 |
+
}
|
248 |
+
},
|
249 |
+
"versions": {
|
250 |
+
"paws_de": 0.0,
|
251 |
+
"paws_en": 0.0,
|
252 |
+
"paws_es": 0.0,
|
253 |
+
"paws_fr": 0.0,
|
254 |
+
"paws_ja": 0.0,
|
255 |
+
"paws_ko": 0.0,
|
256 |
+
"paws_zh": 0.0,
|
257 |
+
"pawsx": "N/A"
|
258 |
+
},
|
259 |
+
"n-shot": {
|
260 |
+
"paws_de": 0,
|
261 |
+
"paws_en": 0,
|
262 |
+
"paws_es": 0,
|
263 |
+
"paws_fr": 0,
|
264 |
+
"paws_ja": 0,
|
265 |
+
"paws_ko": 0,
|
266 |
+
"paws_zh": 0,
|
267 |
+
"pawsx": 0
|
268 |
+
},
|
269 |
+
"config": {
|
270 |
+
"model": "hf",
|
271 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
272 |
+
"batch_size": "auto",
|
273 |
+
"batch_sizes": [
|
274 |
+
64
|
275 |
+
],
|
276 |
+
"device": null,
|
277 |
+
"use_cache": null,
|
278 |
+
"limit": null,
|
279 |
+
"bootstrap_iters": 100000,
|
280 |
+
"gen_kwargs": null
|
281 |
+
},
|
282 |
+
"git_hash": "1ee41f7"
|
283 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9c5d9698f108e9c4a08386a4c539ee310c0cf8f16ec27e15a1640e297fde8df5
|
3 |
+
size 60320
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"piqa": {
|
4 |
+
"acc,none": 0.7110990206746464,
|
5 |
+
"acc_stderr,none": 0.010575111841364906,
|
6 |
+
"acc_norm,none": 0.7138193688792165,
|
7 |
+
"acc_norm_stderr,none": 0.010545318576106643,
|
8 |
+
"alias": "piqa"
|
9 |
+
}
|
10 |
+
},
|
11 |
+
"configs": {
|
12 |
+
"piqa": {
|
13 |
+
"task": "piqa",
|
14 |
+
"dataset_path": "piqa",
|
15 |
+
"training_split": "train",
|
16 |
+
"validation_split": "validation",
|
17 |
+
"doc_to_text": "Question: {{goal}}\nAnswer:",
|
18 |
+
"doc_to_target": "label",
|
19 |
+
"doc_to_choice": "{{[sol1, sol2]}}",
|
20 |
+
"description": "",
|
21 |
+
"target_delimiter": " ",
|
22 |
+
"fewshot_delimiter": "\n\n",
|
23 |
+
"metric_list": [
|
24 |
+
{
|
25 |
+
"metric": "acc",
|
26 |
+
"aggregation": "mean",
|
27 |
+
"higher_is_better": true
|
28 |
+
},
|
29 |
+
{
|
30 |
+
"metric": "acc_norm",
|
31 |
+
"aggregation": "mean",
|
32 |
+
"higher_is_better": true
|
33 |
+
}
|
34 |
+
],
|
35 |
+
"output_type": "multiple_choice",
|
36 |
+
"repeats": 1,
|
37 |
+
"should_decontaminate": true,
|
38 |
+
"doc_to_decontamination_query": "goal",
|
39 |
+
"metadata": {
|
40 |
+
"version": 1.0
|
41 |
+
}
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"versions": {
|
45 |
+
"piqa": 1.0
|
46 |
+
},
|
47 |
+
"n-shot": {
|
48 |
+
"piqa": 0
|
49 |
+
},
|
50 |
+
"config": {
|
51 |
+
"model": "hf",
|
52 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
53 |
+
"batch_size": "auto",
|
54 |
+
"batch_sizes": [
|
55 |
+
64
|
56 |
+
],
|
57 |
+
"device": null,
|
58 |
+
"use_cache": null,
|
59 |
+
"limit": null,
|
60 |
+
"bootstrap_iters": 100000,
|
61 |
+
"gen_kwargs": null
|
62 |
+
},
|
63 |
+
"git_hash": "1ee41f7"
|
64 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9cf717c165ed5b4ffa6a2c2390c31bf01b678ea3eea2586b1cf8095a63c329ca
|
3 |
+
size 37012
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f867471b2e1ce25f12ee05cc4685e1d02cb1dd75ddcae5cd0ca561f8715d5748
|
3 |
+
size 463796
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,67 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"record": {
|
4 |
+
"f1,none": 0.26163523828089236,
|
5 |
+
"f1_stderr,none": 0.004364439540718011,
|
6 |
+
"em,none": 0.254,
|
7 |
+
"em_stderr,none": 0.004353193658626019,
|
8 |
+
"alias": "record"
|
9 |
+
}
|
10 |
+
},
|
11 |
+
"configs": {
|
12 |
+
"record": {
|
13 |
+
"task": "record",
|
14 |
+
"group": [
|
15 |
+
"super-glue-lm-eval-v1"
|
16 |
+
],
|
17 |
+
"dataset_path": "super_glue",
|
18 |
+
"dataset_name": "record",
|
19 |
+
"training_split": "train",
|
20 |
+
"validation_split": "validation",
|
21 |
+
"doc_to_text": "def doc_to_text(doc):\n initial_text, *highlights = doc[\"passage\"].strip().split(\"\\n@highlight\\n\")\n text = initial_text + \"\\n\\n\"\n for highlight in highlights:\n text += f\" - {highlight}.\\n\"\n return text\n",
|
22 |
+
"doc_to_target": "{{answers}}",
|
23 |
+
"doc_to_choice": "{{entities}}",
|
24 |
+
"process_results": "def process_results(doc, results):\n # ReCoRD's evaluation is actually deceptively simple:\n # - Pick the maximum likelihood prediction entity\n # - Evaluate the accuracy and token F1 PER EXAMPLE\n # - Average over all examples\n max_idx = np.argmax(np.array([result[0] for result in results]))\n\n prediction = doc[\"entities\"][max_idx]\n gold_label_set = doc[\"answers\"]\n f1 = metric_max_over_ground_truths(\n squad_metrics.compute_f1, prediction, gold_label_set\n )\n em = metric_max_over_ground_truths(\n squad_metrics.compute_exact, prediction, gold_label_set\n )\n\n return {\n \"f1\": f1,\n \"em\": em,\n }\n",
|
25 |
+
"description": "",
|
26 |
+
"target_delimiter": " ",
|
27 |
+
"fewshot_delimiter": "\n\n",
|
28 |
+
"metric_list": [
|
29 |
+
{
|
30 |
+
"metric": "f1",
|
31 |
+
"aggregation": "mean"
|
32 |
+
},
|
33 |
+
{
|
34 |
+
"metric": "em",
|
35 |
+
"higher_is_better": true,
|
36 |
+
"aggregation": "mean"
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"output_type": "multiple_choice",
|
40 |
+
"repeats": 1,
|
41 |
+
"should_decontaminate": false,
|
42 |
+
"metadata": {
|
43 |
+
"version": 1.0
|
44 |
+
}
|
45 |
+
}
|
46 |
+
},
|
47 |
+
"versions": {
|
48 |
+
"record": 1.0
|
49 |
+
},
|
50 |
+
"n-shot": {
|
51 |
+
"record": 0
|
52 |
+
},
|
53 |
+
"config": {
|
54 |
+
"model": "hf",
|
55 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
56 |
+
"batch_size": "auto",
|
57 |
+
"batch_sizes": [
|
58 |
+
32
|
59 |
+
],
|
60 |
+
"device": null,
|
61 |
+
"use_cache": null,
|
62 |
+
"limit": null,
|
63 |
+
"bootstrap_iters": 100000,
|
64 |
+
"gen_kwargs": null
|
65 |
+
},
|
66 |
+
"git_hash": "1ee41f7"
|
67 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:59c4287671b2933f98fdef86dd3bb0d0138e9b3ff34bc78708047d70497620c3
|
3 |
+
size 66555
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"sciq": {
|
4 |
+
"acc,none": 0.899,
|
5 |
+
"acc_stderr,none": 0.009533618929340983,
|
6 |
+
"acc_norm,none": 0.853,
|
7 |
+
"acc_norm_stderr,none": 0.011203415395160335,
|
8 |
+
"alias": "sciq"
|
9 |
+
}
|
10 |
+
},
|
11 |
+
"configs": {
|
12 |
+
"sciq": {
|
13 |
+
"task": "sciq",
|
14 |
+
"dataset_path": "sciq",
|
15 |
+
"training_split": "train",
|
16 |
+
"validation_split": "validation",
|
17 |
+
"test_split": "test",
|
18 |
+
"doc_to_text": "{{support.lstrip()}}\nQuestion: {{question}}\nAnswer:",
|
19 |
+
"doc_to_target": 3,
|
20 |
+
"doc_to_choice": "{{[distractor1, distractor2, distractor3, correct_answer]}}",
|
21 |
+
"description": "",
|
22 |
+
"target_delimiter": " ",
|
23 |
+
"fewshot_delimiter": "\n\n",
|
24 |
+
"metric_list": [
|
25 |
+
{
|
26 |
+
"metric": "acc",
|
27 |
+
"aggregation": "mean",
|
28 |
+
"higher_is_better": true
|
29 |
+
},
|
30 |
+
{
|
31 |
+
"metric": "acc_norm",
|
32 |
+
"aggregation": "mean",
|
33 |
+
"higher_is_better": true
|
34 |
+
}
|
35 |
+
],
|
36 |
+
"output_type": "multiple_choice",
|
37 |
+
"repeats": 1,
|
38 |
+
"should_decontaminate": true,
|
39 |
+
"doc_to_decontamination_query": "{{support}} {{question}}",
|
40 |
+
"metadata": {
|
41 |
+
"version": 1.0
|
42 |
+
}
|
43 |
+
}
|
44 |
+
},
|
45 |
+
"versions": {
|
46 |
+
"sciq": 1.0
|
47 |
+
},
|
48 |
+
"n-shot": {
|
49 |
+
"sciq": 0
|
50 |
+
},
|
51 |
+
"config": {
|
52 |
+
"model": "hf",
|
53 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
54 |
+
"batch_size": "auto",
|
55 |
+
"batch_sizes": [
|
56 |
+
64
|
57 |
+
],
|
58 |
+
"device": null,
|
59 |
+
"use_cache": null,
|
60 |
+
"limit": null,
|
61 |
+
"bootstrap_iters": 100000,
|
62 |
+
"gen_kwargs": null
|
63 |
+
},
|
64 |
+
"git_hash": "1ee41f7"
|
65 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:645ee047914143613682070fde4ab80b202381552292a4937a2aaa9963b8e56f
|
3 |
+
size 45089
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"winogrande": {
|
4 |
+
"acc,none": 0.5935280189423836,
|
5 |
+
"acc_stderr,none": 0.013804448697753378,
|
6 |
+
"alias": "winogrande"
|
7 |
+
}
|
8 |
+
},
|
9 |
+
"configs": {
|
10 |
+
"winogrande": {
|
11 |
+
"task": "winogrande",
|
12 |
+
"dataset_path": "winogrande",
|
13 |
+
"dataset_name": "winogrande_xl",
|
14 |
+
"training_split": "train",
|
15 |
+
"validation_split": "validation",
|
16 |
+
"doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
17 |
+
"doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
18 |
+
"doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
19 |
+
"description": "",
|
20 |
+
"target_delimiter": " ",
|
21 |
+
"fewshot_delimiter": "\n\n",
|
22 |
+
"metric_list": [
|
23 |
+
{
|
24 |
+
"metric": "acc",
|
25 |
+
"aggregation": "mean",
|
26 |
+
"higher_is_better": true
|
27 |
+
}
|
28 |
+
],
|
29 |
+
"output_type": "multiple_choice",
|
30 |
+
"repeats": 1,
|
31 |
+
"should_decontaminate": true,
|
32 |
+
"doc_to_decontamination_query": "sentence",
|
33 |
+
"metadata": {
|
34 |
+
"version": 1.0
|
35 |
+
}
|
36 |
+
}
|
37 |
+
},
|
38 |
+
"versions": {
|
39 |
+
"winogrande": 1.0
|
40 |
+
},
|
41 |
+
"n-shot": {
|
42 |
+
"winogrande": 0
|
43 |
+
},
|
44 |
+
"config": {
|
45 |
+
"model": "hf",
|
46 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
47 |
+
"batch_size": "auto",
|
48 |
+
"batch_sizes": [
|
49 |
+
64
|
50 |
+
],
|
51 |
+
"device": null,
|
52 |
+
"use_cache": null,
|
53 |
+
"limit": null,
|
54 |
+
"bootstrap_iters": 100000,
|
55 |
+
"gen_kwargs": null
|
56 |
+
},
|
57 |
+
"git_hash": "1ee41f7"
|
58 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d134dd27739241cc1f7433415c1a7eecad07fae73514e0108e8948179a432203
|
3 |
+
size 37250
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,390 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"xcopa": {
|
4 |
+
"acc,none": 0.5787272727272728,
|
5 |
+
"acc_stderr,none": 0.04424725212711732,
|
6 |
+
"alias": "xcopa"
|
7 |
+
},
|
8 |
+
"xcopa_et": {
|
9 |
+
"acc,none": 0.57,
|
10 |
+
"acc_stderr,none": 0.02216263442665284,
|
11 |
+
"alias": " - xcopa_et"
|
12 |
+
},
|
13 |
+
"xcopa_ht": {
|
14 |
+
"acc,none": 0.508,
|
15 |
+
"acc_stderr,none": 0.022380208834928028,
|
16 |
+
"alias": " - xcopa_ht"
|
17 |
+
},
|
18 |
+
"xcopa_id": {
|
19 |
+
"acc,none": 0.636,
|
20 |
+
"acc_stderr,none": 0.021539170637317688,
|
21 |
+
"alias": " - xcopa_id"
|
22 |
+
},
|
23 |
+
"xcopa_it": {
|
24 |
+
"acc,none": 0.638,
|
25 |
+
"acc_stderr,none": 0.021513662527582404,
|
26 |
+
"alias": " - xcopa_it"
|
27 |
+
},
|
28 |
+
"xcopa_qu": {
|
29 |
+
"acc,none": 0.518,
|
30 |
+
"acc_stderr,none": 0.02236856511738799,
|
31 |
+
"alias": " - xcopa_qu"
|
32 |
+
},
|
33 |
+
"xcopa_sw": {
|
34 |
+
"acc,none": 0.562,
|
35 |
+
"acc_stderr,none": 0.022210326363977417,
|
36 |
+
"alias": " - xcopa_sw"
|
37 |
+
},
|
38 |
+
"xcopa_ta": {
|
39 |
+
"acc,none": 0.544,
|
40 |
+
"acc_stderr,none": 0.022296238348407056,
|
41 |
+
"alias": " - xcopa_ta"
|
42 |
+
},
|
43 |
+
"xcopa_th": {
|
44 |
+
"acc,none": 0.566,
|
45 |
+
"acc_stderr,none": 0.022187215803029008,
|
46 |
+
"alias": " - xcopa_th"
|
47 |
+
},
|
48 |
+
"xcopa_tr": {
|
49 |
+
"acc,none": 0.56,
|
50 |
+
"acc_stderr,none": 0.022221331534143036,
|
51 |
+
"alias": " - xcopa_tr"
|
52 |
+
},
|
53 |
+
"xcopa_vi": {
|
54 |
+
"acc,none": 0.612,
|
55 |
+
"acc_stderr,none": 0.02181430098478764,
|
56 |
+
"alias": " - xcopa_vi"
|
57 |
+
},
|
58 |
+
"xcopa_zh": {
|
59 |
+
"acc,none": 0.652,
|
60 |
+
"acc_stderr,none": 0.0213237286328075,
|
61 |
+
"alias": " - xcopa_zh"
|
62 |
+
}
|
63 |
+
},
|
64 |
+
"groups": {
|
65 |
+
"xcopa": {
|
66 |
+
"acc,none": 0.5787272727272728,
|
67 |
+
"acc_stderr,none": 0.04424725212711732,
|
68 |
+
"alias": "xcopa"
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"configs": {
|
72 |
+
"xcopa_et": {
|
73 |
+
"task": "xcopa_et",
|
74 |
+
"group": "xcopa",
|
75 |
+
"dataset_path": "xcopa",
|
76 |
+
"dataset_name": "et",
|
77 |
+
"validation_split": "validation",
|
78 |
+
"test_split": "test",
|
79 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a4dd9d00>, connector={'cause': 'sest', 'effect': 'seetõttu'})",
|
80 |
+
"doc_to_target": "label",
|
81 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
82 |
+
"description": "",
|
83 |
+
"target_delimiter": " ",
|
84 |
+
"fewshot_delimiter": "\n\n",
|
85 |
+
"metric_list": [
|
86 |
+
{
|
87 |
+
"metric": "acc"
|
88 |
+
}
|
89 |
+
],
|
90 |
+
"output_type": "multiple_choice",
|
91 |
+
"repeats": 1,
|
92 |
+
"should_decontaminate": false,
|
93 |
+
"metadata": {
|
94 |
+
"version": 1.0
|
95 |
+
}
|
96 |
+
},
|
97 |
+
"xcopa_ht": {
|
98 |
+
"task": "xcopa_ht",
|
99 |
+
"group": "xcopa",
|
100 |
+
"dataset_path": "xcopa",
|
101 |
+
"dataset_name": "ht",
|
102 |
+
"validation_split": "validation",
|
103 |
+
"test_split": "test",
|
104 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d3f7e0>, connector={'cause': 'poukisa', 'effect': 'donk sa'})",
|
105 |
+
"doc_to_target": "label",
|
106 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
107 |
+
"description": "",
|
108 |
+
"target_delimiter": " ",
|
109 |
+
"fewshot_delimiter": "\n\n",
|
110 |
+
"metric_list": [
|
111 |
+
{
|
112 |
+
"metric": "acc"
|
113 |
+
}
|
114 |
+
],
|
115 |
+
"output_type": "multiple_choice",
|
116 |
+
"repeats": 1,
|
117 |
+
"should_decontaminate": false,
|
118 |
+
"metadata": {
|
119 |
+
"version": 1.0
|
120 |
+
}
|
121 |
+
},
|
122 |
+
"xcopa_id": {
|
123 |
+
"task": "xcopa_id",
|
124 |
+
"group": "xcopa",
|
125 |
+
"dataset_path": "xcopa",
|
126 |
+
"dataset_name": "id",
|
127 |
+
"validation_split": "validation",
|
128 |
+
"test_split": "test",
|
129 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d3d4e0>, connector={'cause': 'karena', 'effect': 'maka'})",
|
130 |
+
"doc_to_target": "label",
|
131 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
132 |
+
"description": "",
|
133 |
+
"target_delimiter": " ",
|
134 |
+
"fewshot_delimiter": "\n\n",
|
135 |
+
"metric_list": [
|
136 |
+
{
|
137 |
+
"metric": "acc"
|
138 |
+
}
|
139 |
+
],
|
140 |
+
"output_type": "multiple_choice",
|
141 |
+
"repeats": 1,
|
142 |
+
"should_decontaminate": false,
|
143 |
+
"metadata": {
|
144 |
+
"version": 1.0
|
145 |
+
}
|
146 |
+
},
|
147 |
+
"xcopa_it": {
|
148 |
+
"task": "xcopa_it",
|
149 |
+
"group": "xcopa",
|
150 |
+
"dataset_path": "xcopa",
|
151 |
+
"dataset_name": "it",
|
152 |
+
"validation_split": "validation",
|
153 |
+
"test_split": "test",
|
154 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d132e0>, connector={'cause': 'perché', 'effect': 'quindi'})",
|
155 |
+
"doc_to_target": "label",
|
156 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
157 |
+
"description": "",
|
158 |
+
"target_delimiter": " ",
|
159 |
+
"fewshot_delimiter": "\n\n",
|
160 |
+
"metric_list": [
|
161 |
+
{
|
162 |
+
"metric": "acc"
|
163 |
+
}
|
164 |
+
],
|
165 |
+
"output_type": "multiple_choice",
|
166 |
+
"repeats": 1,
|
167 |
+
"should_decontaminate": false,
|
168 |
+
"metadata": {
|
169 |
+
"version": 1.0
|
170 |
+
}
|
171 |
+
},
|
172 |
+
"xcopa_qu": {
|
173 |
+
"task": "xcopa_qu",
|
174 |
+
"group": "xcopa",
|
175 |
+
"dataset_path": "xcopa",
|
176 |
+
"dataset_name": "qu",
|
177 |
+
"validation_split": "validation",
|
178 |
+
"test_split": "test",
|
179 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d3eac0>, connector={'cause': 'imataq', 'effect': 'chaymi'})",
|
180 |
+
"doc_to_target": "label",
|
181 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
182 |
+
"description": "",
|
183 |
+
"target_delimiter": " ",
|
184 |
+
"fewshot_delimiter": "\n\n",
|
185 |
+
"metric_list": [
|
186 |
+
{
|
187 |
+
"metric": "acc"
|
188 |
+
}
|
189 |
+
],
|
190 |
+
"output_type": "multiple_choice",
|
191 |
+
"repeats": 1,
|
192 |
+
"should_decontaminate": false,
|
193 |
+
"metadata": {
|
194 |
+
"version": 1.0
|
195 |
+
}
|
196 |
+
},
|
197 |
+
"xcopa_sw": {
|
198 |
+
"task": "xcopa_sw",
|
199 |
+
"group": "xcopa",
|
200 |
+
"dataset_path": "xcopa",
|
201 |
+
"dataset_name": "sw",
|
202 |
+
"validation_split": "validation",
|
203 |
+
"test_split": "test",
|
204 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d12d40>, connector={'cause': 'kwa sababu', 'effect': 'kwa hiyo'})",
|
205 |
+
"doc_to_target": "label",
|
206 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
207 |
+
"description": "",
|
208 |
+
"target_delimiter": " ",
|
209 |
+
"fewshot_delimiter": "\n\n",
|
210 |
+
"metric_list": [
|
211 |
+
{
|
212 |
+
"metric": "acc"
|
213 |
+
}
|
214 |
+
],
|
215 |
+
"output_type": "multiple_choice",
|
216 |
+
"repeats": 1,
|
217 |
+
"should_decontaminate": false,
|
218 |
+
"metadata": {
|
219 |
+
"version": 1.0
|
220 |
+
}
|
221 |
+
},
|
222 |
+
"xcopa_ta": {
|
223 |
+
"task": "xcopa_ta",
|
224 |
+
"group": "xcopa",
|
225 |
+
"dataset_path": "xcopa",
|
226 |
+
"dataset_name": "ta",
|
227 |
+
"validation_split": "validation",
|
228 |
+
"test_split": "test",
|
229 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87c0271300>, connector={'cause': 'காரணமாக', 'effect': 'எனவே'})",
|
230 |
+
"doc_to_target": "label",
|
231 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
232 |
+
"description": "",
|
233 |
+
"target_delimiter": " ",
|
234 |
+
"fewshot_delimiter": "\n\n",
|
235 |
+
"metric_list": [
|
236 |
+
{
|
237 |
+
"metric": "acc"
|
238 |
+
}
|
239 |
+
],
|
240 |
+
"output_type": "multiple_choice",
|
241 |
+
"repeats": 1,
|
242 |
+
"should_decontaminate": false,
|
243 |
+
"metadata": {
|
244 |
+
"version": 1.0
|
245 |
+
}
|
246 |
+
},
|
247 |
+
"xcopa_th": {
|
248 |
+
"task": "xcopa_th",
|
249 |
+
"group": "xcopa",
|
250 |
+
"dataset_path": "xcopa",
|
251 |
+
"dataset_name": "th",
|
252 |
+
"validation_split": "validation",
|
253 |
+
"test_split": "test",
|
254 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d13060>, connector={'cause': 'เพราะ', 'effect': 'ดังนั้น'})",
|
255 |
+
"doc_to_target": "label",
|
256 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
257 |
+
"description": "",
|
258 |
+
"target_delimiter": " ",
|
259 |
+
"fewshot_delimiter": "\n\n",
|
260 |
+
"metric_list": [
|
261 |
+
{
|
262 |
+
"metric": "acc"
|
263 |
+
}
|
264 |
+
],
|
265 |
+
"output_type": "multiple_choice",
|
266 |
+
"repeats": 1,
|
267 |
+
"should_decontaminate": false,
|
268 |
+
"metadata": {
|
269 |
+
"version": 1.0
|
270 |
+
}
|
271 |
+
},
|
272 |
+
"xcopa_tr": {
|
273 |
+
"task": "xcopa_tr",
|
274 |
+
"group": "xcopa",
|
275 |
+
"dataset_path": "xcopa",
|
276 |
+
"dataset_name": "tr",
|
277 |
+
"validation_split": "validation",
|
278 |
+
"test_split": "test",
|
279 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f86ebe98b80>, connector={'cause': 'çünkü', 'effect': 'bu yüzden'})",
|
280 |
+
"doc_to_target": "label",
|
281 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
282 |
+
"description": "",
|
283 |
+
"target_delimiter": " ",
|
284 |
+
"fewshot_delimiter": "\n\n",
|
285 |
+
"metric_list": [
|
286 |
+
{
|
287 |
+
"metric": "acc"
|
288 |
+
}
|
289 |
+
],
|
290 |
+
"output_type": "multiple_choice",
|
291 |
+
"repeats": 1,
|
292 |
+
"should_decontaminate": false,
|
293 |
+
"metadata": {
|
294 |
+
"version": 1.0
|
295 |
+
}
|
296 |
+
},
|
297 |
+
"xcopa_vi": {
|
298 |
+
"task": "xcopa_vi",
|
299 |
+
"group": "xcopa",
|
300 |
+
"dataset_path": "xcopa",
|
301 |
+
"dataset_name": "vi",
|
302 |
+
"validation_split": "validation",
|
303 |
+
"test_split": "test",
|
304 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d3c9a0>, connector={'cause': 'bởi vì', 'effect': 'vì vậy'})",
|
305 |
+
"doc_to_target": "label",
|
306 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
307 |
+
"description": "",
|
308 |
+
"target_delimiter": " ",
|
309 |
+
"fewshot_delimiter": "\n\n",
|
310 |
+
"metric_list": [
|
311 |
+
{
|
312 |
+
"metric": "acc"
|
313 |
+
}
|
314 |
+
],
|
315 |
+
"output_type": "multiple_choice",
|
316 |
+
"repeats": 1,
|
317 |
+
"should_decontaminate": false,
|
318 |
+
"metadata": {
|
319 |
+
"version": 1.0
|
320 |
+
}
|
321 |
+
},
|
322 |
+
"xcopa_zh": {
|
323 |
+
"task": "xcopa_zh",
|
324 |
+
"group": "xcopa",
|
325 |
+
"dataset_path": "xcopa",
|
326 |
+
"dataset_name": "zh",
|
327 |
+
"validation_split": "validation",
|
328 |
+
"test_split": "test",
|
329 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f87a7d122a0>, connector={'cause': '因为', 'effect': '所以'})",
|
330 |
+
"doc_to_target": "label",
|
331 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
332 |
+
"description": "",
|
333 |
+
"target_delimiter": " ",
|
334 |
+
"fewshot_delimiter": "\n\n",
|
335 |
+
"metric_list": [
|
336 |
+
{
|
337 |
+
"metric": "acc"
|
338 |
+
}
|
339 |
+
],
|
340 |
+
"output_type": "multiple_choice",
|
341 |
+
"repeats": 1,
|
342 |
+
"should_decontaminate": false,
|
343 |
+
"metadata": {
|
344 |
+
"version": 1.0
|
345 |
+
}
|
346 |
+
}
|
347 |
+
},
|
348 |
+
"versions": {
|
349 |
+
"xcopa": "N/A",
|
350 |
+
"xcopa_et": 1.0,
|
351 |
+
"xcopa_ht": 1.0,
|
352 |
+
"xcopa_id": 1.0,
|
353 |
+
"xcopa_it": 1.0,
|
354 |
+
"xcopa_qu": 1.0,
|
355 |
+
"xcopa_sw": 1.0,
|
356 |
+
"xcopa_ta": 1.0,
|
357 |
+
"xcopa_th": 1.0,
|
358 |
+
"xcopa_tr": 1.0,
|
359 |
+
"xcopa_vi": 1.0,
|
360 |
+
"xcopa_zh": 1.0
|
361 |
+
},
|
362 |
+
"n-shot": {
|
363 |
+
"xcopa": 0,
|
364 |
+
"xcopa_et": 0,
|
365 |
+
"xcopa_ht": 0,
|
366 |
+
"xcopa_id": 0,
|
367 |
+
"xcopa_it": 0,
|
368 |
+
"xcopa_qu": 0,
|
369 |
+
"xcopa_sw": 0,
|
370 |
+
"xcopa_ta": 0,
|
371 |
+
"xcopa_th": 0,
|
372 |
+
"xcopa_tr": 0,
|
373 |
+
"xcopa_vi": 0,
|
374 |
+
"xcopa_zh": 0
|
375 |
+
},
|
376 |
+
"config": {
|
377 |
+
"model": "hf",
|
378 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
379 |
+
"batch_size": "auto",
|
380 |
+
"batch_sizes": [
|
381 |
+
64
|
382 |
+
],
|
383 |
+
"device": null,
|
384 |
+
"use_cache": null,
|
385 |
+
"limit": null,
|
386 |
+
"bootstrap_iters": 100000,
|
387 |
+
"gen_kwargs": null
|
388 |
+
},
|
389 |
+
"git_hash": "1ee41f7"
|
390 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cd8dad5dc3ea2c5d0fd99e6decbe8ee0bf0ad0f41f4f95b6bc45a59173c6506c
|
3 |
+
size 79942
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,548 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"xnli": {
|
4 |
+
"acc,none": 0.4044979919678715,
|
5 |
+
"acc_stderr,none": 0.04620022346504284,
|
6 |
+
"alias": "xnli"
|
7 |
+
},
|
8 |
+
"xnli_ar": {
|
9 |
+
"acc,none": 0.3345381526104418,
|
10 |
+
"acc_stderr,none": 0.009457404390939166,
|
11 |
+
"alias": " - xnli_ar"
|
12 |
+
},
|
13 |
+
"xnli_bg": {
|
14 |
+
"acc,none": 0.42610441767068274,
|
15 |
+
"acc_stderr,none": 0.009912016377459067,
|
16 |
+
"alias": " - xnli_bg"
|
17 |
+
},
|
18 |
+
"xnli_de": {
|
19 |
+
"acc,none": 0.44859437751004017,
|
20 |
+
"acc_stderr,none": 0.009968964736894263,
|
21 |
+
"alias": " - xnli_de"
|
22 |
+
},
|
23 |
+
"xnli_el": {
|
24 |
+
"acc,none": 0.37349397590361444,
|
25 |
+
"acc_stderr,none": 0.00969598596221976,
|
26 |
+
"alias": " - xnli_el"
|
27 |
+
},
|
28 |
+
"xnli_en": {
|
29 |
+
"acc,none": 0.5108433734939759,
|
30 |
+
"acc_stderr,none": 0.010019715824483473,
|
31 |
+
"alias": " - xnli_en"
|
32 |
+
},
|
33 |
+
"xnli_es": {
|
34 |
+
"acc,none": 0.4566265060240964,
|
35 |
+
"acc_stderr,none": 0.009984293410840315,
|
36 |
+
"alias": " - xnli_es"
|
37 |
+
},
|
38 |
+
"xnli_fr": {
|
39 |
+
"acc,none": 0.457429718875502,
|
40 |
+
"acc_stderr,none": 0.009985682220227464,
|
41 |
+
"alias": " - xnli_fr"
|
42 |
+
},
|
43 |
+
"xnli_hi": {
|
44 |
+
"acc,none": 0.3682730923694779,
|
45 |
+
"acc_stderr,none": 0.009668013178998446,
|
46 |
+
"alias": " - xnli_hi"
|
47 |
+
},
|
48 |
+
"xnli_ru": {
|
49 |
+
"acc,none": 0.4493975903614458,
|
50 |
+
"acc_stderr,none": 0.009970615649588139,
|
51 |
+
"alias": " - xnli_ru"
|
52 |
+
},
|
53 |
+
"xnli_sw": {
|
54 |
+
"acc,none": 0.3357429718875502,
|
55 |
+
"acc_stderr,none": 0.009465838617337356,
|
56 |
+
"alias": " - xnli_sw"
|
57 |
+
},
|
58 |
+
"xnli_th": {
|
59 |
+
"acc,none": 0.38473895582329315,
|
60 |
+
"acc_stderr,none": 0.00975214930715253,
|
61 |
+
"alias": " - xnli_th"
|
62 |
+
},
|
63 |
+
"xnli_tr": {
|
64 |
+
"acc,none": 0.39799196787148594,
|
65 |
+
"acc_stderr,none": 0.009811284026425582,
|
66 |
+
"alias": " - xnli_tr"
|
67 |
+
},
|
68 |
+
"xnli_ur": {
|
69 |
+
"acc,none": 0.3506024096385542,
|
70 |
+
"acc_stderr,none": 0.009564237156206098,
|
71 |
+
"alias": " - xnli_ur"
|
72 |
+
},
|
73 |
+
"xnli_vi": {
|
74 |
+
"acc,none": 0.43052208835341366,
|
75 |
+
"acc_stderr,none": 0.009924844537285524,
|
76 |
+
"alias": " - xnli_vi"
|
77 |
+
},
|
78 |
+
"xnli_zh": {
|
79 |
+
"acc,none": 0.342570281124498,
|
80 |
+
"acc_stderr,none": 0.009512333319470373,
|
81 |
+
"alias": " - xnli_zh"
|
82 |
+
}
|
83 |
+
},
|
84 |
+
"groups": {
|
85 |
+
"xnli": {
|
86 |
+
"acc,none": 0.4044979919678715,
|
87 |
+
"acc_stderr,none": 0.04620022346504284,
|
88 |
+
"alias": "xnli"
|
89 |
+
}
|
90 |
+
},
|
91 |
+
"configs": {
|
92 |
+
"xnli_ar": {
|
93 |
+
"task": "xnli_ar",
|
94 |
+
"group": "xnli",
|
95 |
+
"dataset_path": "xnli",
|
96 |
+
"dataset_name": "ar",
|
97 |
+
"training_split": "train",
|
98 |
+
"validation_split": "validation",
|
99 |
+
"doc_to_text": "",
|
100 |
+
"doc_to_target": "label",
|
101 |
+
"doc_to_choice": "{{[premise+\", صحيح? نعم, \"+hypothesis,premise+\", صحيح? لذا, \"+hypothesis,premise+\", صحيح? رقم, \"+hypothesis]}}",
|
102 |
+
"description": "",
|
103 |
+
"target_delimiter": " ",
|
104 |
+
"fewshot_delimiter": "\n\n",
|
105 |
+
"metric_list": [
|
106 |
+
{
|
107 |
+
"metric": "acc",
|
108 |
+
"aggregation": "mean",
|
109 |
+
"higher_is_better": true
|
110 |
+
}
|
111 |
+
],
|
112 |
+
"output_type": "multiple_choice",
|
113 |
+
"repeats": 1,
|
114 |
+
"should_decontaminate": false,
|
115 |
+
"metadata": {
|
116 |
+
"version": 1.0
|
117 |
+
}
|
118 |
+
},
|
119 |
+
"xnli_bg": {
|
120 |
+
"task": "xnli_bg",
|
121 |
+
"group": "xnli",
|
122 |
+
"dataset_path": "xnli",
|
123 |
+
"dataset_name": "bg",
|
124 |
+
"training_split": "train",
|
125 |
+
"validation_split": "validation",
|
126 |
+
"doc_to_text": "",
|
127 |
+
"doc_to_target": "label",
|
128 |
+
"doc_to_choice": "{{[premise+\", правилно? да, \"+hypothesis,premise+\", правилно? така, \"+hypothesis,premise+\", правилно? не, \"+hypothesis]}}",
|
129 |
+
"description": "",
|
130 |
+
"target_delimiter": " ",
|
131 |
+
"fewshot_delimiter": "\n\n",
|
132 |
+
"metric_list": [
|
133 |
+
{
|
134 |
+
"metric": "acc",
|
135 |
+
"aggregation": "mean",
|
136 |
+
"higher_is_better": true
|
137 |
+
}
|
138 |
+
],
|
139 |
+
"output_type": "multiple_choice",
|
140 |
+
"repeats": 1,
|
141 |
+
"should_decontaminate": false,
|
142 |
+
"metadata": {
|
143 |
+
"version": 1.0
|
144 |
+
}
|
145 |
+
},
|
146 |
+
"xnli_de": {
|
147 |
+
"task": "xnli_de",
|
148 |
+
"group": "xnli",
|
149 |
+
"dataset_path": "xnli",
|
150 |
+
"dataset_name": "de",
|
151 |
+
"training_split": "train",
|
152 |
+
"validation_split": "validation",
|
153 |
+
"doc_to_text": "",
|
154 |
+
"doc_to_target": "label",
|
155 |
+
"doc_to_choice": "{{[premise+\", richtig? Ja, \"+hypothesis,premise+\", richtig? Auch, \"+hypothesis,premise+\", richtig? Nein, \"+hypothesis]}}",
|
156 |
+
"description": "",
|
157 |
+
"target_delimiter": " ",
|
158 |
+
"fewshot_delimiter": "\n\n",
|
159 |
+
"metric_list": [
|
160 |
+
{
|
161 |
+
"metric": "acc",
|
162 |
+
"aggregation": "mean",
|
163 |
+
"higher_is_better": true
|
164 |
+
}
|
165 |
+
],
|
166 |
+
"output_type": "multiple_choice",
|
167 |
+
"repeats": 1,
|
168 |
+
"should_decontaminate": false,
|
169 |
+
"metadata": {
|
170 |
+
"version": 1.0
|
171 |
+
}
|
172 |
+
},
|
173 |
+
"xnli_el": {
|
174 |
+
"task": "xnli_el",
|
175 |
+
"group": "xnli",
|
176 |
+
"dataset_path": "xnli",
|
177 |
+
"dataset_name": "el",
|
178 |
+
"training_split": "train",
|
179 |
+
"validation_split": "validation",
|
180 |
+
"doc_to_text": "",
|
181 |
+
"doc_to_target": "label",
|
182 |
+
"doc_to_choice": "{{[premise+\", σωστός? Ναί, \"+hypothesis,premise+\", σωστός? Έτσι, \"+hypothesis,premise+\", σωστός? όχι, \"+hypothesis]}}",
|
183 |
+
"description": "",
|
184 |
+
"target_delimiter": " ",
|
185 |
+
"fewshot_delimiter": "\n\n",
|
186 |
+
"metric_list": [
|
187 |
+
{
|
188 |
+
"metric": "acc",
|
189 |
+
"aggregation": "mean",
|
190 |
+
"higher_is_better": true
|
191 |
+
}
|
192 |
+
],
|
193 |
+
"output_type": "multiple_choice",
|
194 |
+
"repeats": 1,
|
195 |
+
"should_decontaminate": false,
|
196 |
+
"metadata": {
|
197 |
+
"version": 1.0
|
198 |
+
}
|
199 |
+
},
|
200 |
+
"xnli_en": {
|
201 |
+
"task": "xnli_en",
|
202 |
+
"group": "xnli",
|
203 |
+
"dataset_path": "xnli",
|
204 |
+
"dataset_name": "en",
|
205 |
+
"training_split": "train",
|
206 |
+
"validation_split": "validation",
|
207 |
+
"doc_to_text": "",
|
208 |
+
"doc_to_target": "label",
|
209 |
+
"doc_to_choice": "{{[premise+\", right? Yes, \"+hypothesis,premise+\", right? Also, \"+hypothesis,premise+\", right? No, \"+hypothesis]}}",
|
210 |
+
"description": "",
|
211 |
+
"target_delimiter": " ",
|
212 |
+
"fewshot_delimiter": "\n\n",
|
213 |
+
"metric_list": [
|
214 |
+
{
|
215 |
+
"metric": "acc",
|
216 |
+
"aggregation": "mean",
|
217 |
+
"higher_is_better": true
|
218 |
+
}
|
219 |
+
],
|
220 |
+
"output_type": "multiple_choice",
|
221 |
+
"repeats": 1,
|
222 |
+
"should_decontaminate": false,
|
223 |
+
"metadata": {
|
224 |
+
"version": 1.0
|
225 |
+
}
|
226 |
+
},
|
227 |
+
"xnli_es": {
|
228 |
+
"task": "xnli_es",
|
229 |
+
"group": "xnli",
|
230 |
+
"dataset_path": "xnli",
|
231 |
+
"dataset_name": "es",
|
232 |
+
"training_split": "train",
|
233 |
+
"validation_split": "validation",
|
234 |
+
"doc_to_text": "",
|
235 |
+
"doc_to_target": "label",
|
236 |
+
"doc_to_choice": "{{[premise+\", correcto? Sí, \"+hypothesis,premise+\", correcto? Asi que, \"+hypothesis,premise+\", correcto? No, \"+hypothesis]}}",
|
237 |
+
"description": "",
|
238 |
+
"target_delimiter": " ",
|
239 |
+
"fewshot_delimiter": "\n\n",
|
240 |
+
"metric_list": [
|
241 |
+
{
|
242 |
+
"metric": "acc",
|
243 |
+
"aggregation": "mean",
|
244 |
+
"higher_is_better": true
|
245 |
+
}
|
246 |
+
],
|
247 |
+
"output_type": "multiple_choice",
|
248 |
+
"repeats": 1,
|
249 |
+
"should_decontaminate": false,
|
250 |
+
"metadata": {
|
251 |
+
"version": 1.0
|
252 |
+
}
|
253 |
+
},
|
254 |
+
"xnli_fr": {
|
255 |
+
"task": "xnli_fr",
|
256 |
+
"group": "xnli",
|
257 |
+
"dataset_path": "xnli",
|
258 |
+
"dataset_name": "fr",
|
259 |
+
"training_split": "train",
|
260 |
+
"validation_split": "validation",
|
261 |
+
"doc_to_text": "",
|
262 |
+
"doc_to_target": "label",
|
263 |
+
"doc_to_choice": "{{[premise+\", correct? Oui, \"+hypothesis,premise+\", correct? Aussi, \"+hypothesis,premise+\", correct? Non, \"+hypothesis]}}",
|
264 |
+
"description": "",
|
265 |
+
"target_delimiter": " ",
|
266 |
+
"fewshot_delimiter": "\n\n",
|
267 |
+
"metric_list": [
|
268 |
+
{
|
269 |
+
"metric": "acc",
|
270 |
+
"aggregation": "mean",
|
271 |
+
"higher_is_better": true
|
272 |
+
}
|
273 |
+
],
|
274 |
+
"output_type": "multiple_choice",
|
275 |
+
"repeats": 1,
|
276 |
+
"should_decontaminate": false,
|
277 |
+
"metadata": {
|
278 |
+
"version": 1.0
|
279 |
+
}
|
280 |
+
},
|
281 |
+
"xnli_hi": {
|
282 |
+
"task": "xnli_hi",
|
283 |
+
"group": "xnli",
|
284 |
+
"dataset_path": "xnli",
|
285 |
+
"dataset_name": "hi",
|
286 |
+
"training_split": "train",
|
287 |
+
"validation_split": "validation",
|
288 |
+
"doc_to_text": "",
|
289 |
+
"doc_to_target": "label",
|
290 |
+
"doc_to_choice": "{{[premise+\", सही? हाँ, \"+hypothesis,premise+\", सही? इसलिए, \"+hypothesis,premise+\", सही? नहीं, \"+hypothesis]}}",
|
291 |
+
"description": "",
|
292 |
+
"target_delimiter": " ",
|
293 |
+
"fewshot_delimiter": "\n\n",
|
294 |
+
"metric_list": [
|
295 |
+
{
|
296 |
+
"metric": "acc",
|
297 |
+
"aggregation": "mean",
|
298 |
+
"higher_is_better": true
|
299 |
+
}
|
300 |
+
],
|
301 |
+
"output_type": "multiple_choice",
|
302 |
+
"repeats": 1,
|
303 |
+
"should_decontaminate": false,
|
304 |
+
"metadata": {
|
305 |
+
"version": 1.0
|
306 |
+
}
|
307 |
+
},
|
308 |
+
"xnli_ru": {
|
309 |
+
"task": "xnli_ru",
|
310 |
+
"group": "xnli",
|
311 |
+
"dataset_path": "xnli",
|
312 |
+
"dataset_name": "ru",
|
313 |
+
"training_split": "train",
|
314 |
+
"validation_split": "validation",
|
315 |
+
"doc_to_text": "",
|
316 |
+
"doc_to_target": "label",
|
317 |
+
"doc_to_choice": "{{[premise+\", правильно? Да, \"+hypothesis,premise+\", правильно? Так, \"+hypothesis,premise+\", правильно? Нет, \"+hypothesis]}}",
|
318 |
+
"description": "",
|
319 |
+
"target_delimiter": " ",
|
320 |
+
"fewshot_delimiter": "\n\n",
|
321 |
+
"metric_list": [
|
322 |
+
{
|
323 |
+
"metric": "acc",
|
324 |
+
"aggregation": "mean",
|
325 |
+
"higher_is_better": true
|
326 |
+
}
|
327 |
+
],
|
328 |
+
"output_type": "multiple_choice",
|
329 |
+
"repeats": 1,
|
330 |
+
"should_decontaminate": false,
|
331 |
+
"metadata": {
|
332 |
+
"version": 1.0
|
333 |
+
}
|
334 |
+
},
|
335 |
+
"xnli_sw": {
|
336 |
+
"task": "xnli_sw",
|
337 |
+
"group": "xnli",
|
338 |
+
"dataset_path": "xnli",
|
339 |
+
"dataset_name": "sw",
|
340 |
+
"training_split": "train",
|
341 |
+
"validation_split": "validation",
|
342 |
+
"doc_to_text": "",
|
343 |
+
"doc_to_target": "label",
|
344 |
+
"doc_to_choice": "{{[premise+\", sahihi? Ndiyo, \"+hypothesis,premise+\", sahihi? Hivyo, \"+hypothesis,premise+\", sahihi? Hapana, \"+hypothesis]}}",
|
345 |
+
"description": "",
|
346 |
+
"target_delimiter": " ",
|
347 |
+
"fewshot_delimiter": "\n\n",
|
348 |
+
"metric_list": [
|
349 |
+
{
|
350 |
+
"metric": "acc",
|
351 |
+
"aggregation": "mean",
|
352 |
+
"higher_is_better": true
|
353 |
+
}
|
354 |
+
],
|
355 |
+
"output_type": "multiple_choice",
|
356 |
+
"repeats": 1,
|
357 |
+
"should_decontaminate": false,
|
358 |
+
"metadata": {
|
359 |
+
"version": 1.0
|
360 |
+
}
|
361 |
+
},
|
362 |
+
"xnli_th": {
|
363 |
+
"task": "xnli_th",
|
364 |
+
"group": "xnli",
|
365 |
+
"dataset_path": "xnli",
|
366 |
+
"dataset_name": "th",
|
367 |
+
"training_split": "train",
|
368 |
+
"validation_split": "validation",
|
369 |
+
"doc_to_text": "",
|
370 |
+
"doc_to_target": "label",
|
371 |
+
"doc_to_choice": "{{[premise+\", ถูกต้อง? ใช่, \"+hypothesis,premise+\", ถูกต้อง? ดังนั้น, \"+hypothesis,premise+\", ถูกต้อง? ไม่, \"+hypothesis]}}",
|
372 |
+
"description": "",
|
373 |
+
"target_delimiter": " ",
|
374 |
+
"fewshot_delimiter": "\n\n",
|
375 |
+
"metric_list": [
|
376 |
+
{
|
377 |
+
"metric": "acc",
|
378 |
+
"aggregation": "mean",
|
379 |
+
"higher_is_better": true
|
380 |
+
}
|
381 |
+
],
|
382 |
+
"output_type": "multiple_choice",
|
383 |
+
"repeats": 1,
|
384 |
+
"should_decontaminate": false,
|
385 |
+
"metadata": {
|
386 |
+
"version": 1.0
|
387 |
+
}
|
388 |
+
},
|
389 |
+
"xnli_tr": {
|
390 |
+
"task": "xnli_tr",
|
391 |
+
"group": "xnli",
|
392 |
+
"dataset_path": "xnli",
|
393 |
+
"dataset_name": "tr",
|
394 |
+
"training_split": "train",
|
395 |
+
"validation_split": "validation",
|
396 |
+
"doc_to_text": "",
|
397 |
+
"doc_to_target": "label",
|
398 |
+
"doc_to_choice": "{{[premise+\", doğru? Evet, \"+hypothesis,premise+\", doğru? Böylece, \"+hypothesis,premise+\", doğru? Hayır, \"+hypothesis]}}",
|
399 |
+
"description": "",
|
400 |
+
"target_delimiter": " ",
|
401 |
+
"fewshot_delimiter": "\n\n",
|
402 |
+
"metric_list": [
|
403 |
+
{
|
404 |
+
"metric": "acc",
|
405 |
+
"aggregation": "mean",
|
406 |
+
"higher_is_better": true
|
407 |
+
}
|
408 |
+
],
|
409 |
+
"output_type": "multiple_choice",
|
410 |
+
"repeats": 1,
|
411 |
+
"should_decontaminate": false,
|
412 |
+
"metadata": {
|
413 |
+
"version": 1.0
|
414 |
+
}
|
415 |
+
},
|
416 |
+
"xnli_ur": {
|
417 |
+
"task": "xnli_ur",
|
418 |
+
"group": "xnli",
|
419 |
+
"dataset_path": "xnli",
|
420 |
+
"dataset_name": "ur",
|
421 |
+
"training_split": "train",
|
422 |
+
"validation_split": "validation",
|
423 |
+
"doc_to_text": "",
|
424 |
+
"doc_to_target": "label",
|
425 |
+
"doc_to_choice": "{{[premise+\", صحیح? جی ہاں, \"+hypothesis,premise+\", صحیح? اس لئے, \"+hypothesis,premise+\", صحیح? نہیں, \"+hypothesis]}}",
|
426 |
+
"description": "",
|
427 |
+
"target_delimiter": " ",
|
428 |
+
"fewshot_delimiter": "\n\n",
|
429 |
+
"metric_list": [
|
430 |
+
{
|
431 |
+
"metric": "acc",
|
432 |
+
"aggregation": "mean",
|
433 |
+
"higher_is_better": true
|
434 |
+
}
|
435 |
+
],
|
436 |
+
"output_type": "multiple_choice",
|
437 |
+
"repeats": 1,
|
438 |
+
"should_decontaminate": false,
|
439 |
+
"metadata": {
|
440 |
+
"version": 1.0
|
441 |
+
}
|
442 |
+
},
|
443 |
+
"xnli_vi": {
|
444 |
+
"task": "xnli_vi",
|
445 |
+
"group": "xnli",
|
446 |
+
"dataset_path": "xnli",
|
447 |
+
"dataset_name": "vi",
|
448 |
+
"training_split": "train",
|
449 |
+
"validation_split": "validation",
|
450 |
+
"doc_to_text": "",
|
451 |
+
"doc_to_target": "label",
|
452 |
+
"doc_to_choice": "{{[premise+\", đúng? Vâng, \"+hypothesis,premise+\", đúng? Vì vậy, \"+hypothesis,premise+\", đúng? Không, \"+hypothesis]}}",
|
453 |
+
"description": "",
|
454 |
+
"target_delimiter": " ",
|
455 |
+
"fewshot_delimiter": "\n\n",
|
456 |
+
"metric_list": [
|
457 |
+
{
|
458 |
+
"metric": "acc",
|
459 |
+
"aggregation": "mean",
|
460 |
+
"higher_is_better": true
|
461 |
+
}
|
462 |
+
],
|
463 |
+
"output_type": "multiple_choice",
|
464 |
+
"repeats": 1,
|
465 |
+
"should_decontaminate": false,
|
466 |
+
"metadata": {
|
467 |
+
"version": 1.0
|
468 |
+
}
|
469 |
+
},
|
470 |
+
"xnli_zh": {
|
471 |
+
"task": "xnli_zh",
|
472 |
+
"group": "xnli",
|
473 |
+
"dataset_path": "xnli",
|
474 |
+
"dataset_name": "zh",
|
475 |
+
"training_split": "train",
|
476 |
+
"validation_split": "validation",
|
477 |
+
"doc_to_text": "",
|
478 |
+
"doc_to_target": "label",
|
479 |
+
"doc_to_choice": "{{[premise+\", 正确? 是的, \"+hypothesis,premise+\", 正确? 所以, \"+hypothesis,premise+\", 正确? 不是的, \"+hypothesis]}}",
|
480 |
+
"description": "",
|
481 |
+
"target_delimiter": " ",
|
482 |
+
"fewshot_delimiter": "\n\n",
|
483 |
+
"metric_list": [
|
484 |
+
{
|
485 |
+
"metric": "acc",
|
486 |
+
"aggregation": "mean",
|
487 |
+
"higher_is_better": true
|
488 |
+
}
|
489 |
+
],
|
490 |
+
"output_type": "multiple_choice",
|
491 |
+
"repeats": 1,
|
492 |
+
"should_decontaminate": false,
|
493 |
+
"metadata": {
|
494 |
+
"version": 1.0
|
495 |
+
}
|
496 |
+
}
|
497 |
+
},
|
498 |
+
"versions": {
|
499 |
+
"xnli": "N/A",
|
500 |
+
"xnli_ar": 1.0,
|
501 |
+
"xnli_bg": 1.0,
|
502 |
+
"xnli_de": 1.0,
|
503 |
+
"xnli_el": 1.0,
|
504 |
+
"xnli_en": 1.0,
|
505 |
+
"xnli_es": 1.0,
|
506 |
+
"xnli_fr": 1.0,
|
507 |
+
"xnli_hi": 1.0,
|
508 |
+
"xnli_ru": 1.0,
|
509 |
+
"xnli_sw": 1.0,
|
510 |
+
"xnli_th": 1.0,
|
511 |
+
"xnli_tr": 1.0,
|
512 |
+
"xnli_ur": 1.0,
|
513 |
+
"xnli_vi": 1.0,
|
514 |
+
"xnli_zh": 1.0
|
515 |
+
},
|
516 |
+
"n-shot": {
|
517 |
+
"xnli": 0,
|
518 |
+
"xnli_ar": 0,
|
519 |
+
"xnli_bg": 0,
|
520 |
+
"xnli_de": 0,
|
521 |
+
"xnli_el": 0,
|
522 |
+
"xnli_en": 0,
|
523 |
+
"xnli_es": 0,
|
524 |
+
"xnli_fr": 0,
|
525 |
+
"xnli_hi": 0,
|
526 |
+
"xnli_ru": 0,
|
527 |
+
"xnli_sw": 0,
|
528 |
+
"xnli_th": 0,
|
529 |
+
"xnli_tr": 0,
|
530 |
+
"xnli_ur": 0,
|
531 |
+
"xnli_vi": 0,
|
532 |
+
"xnli_zh": 0
|
533 |
+
},
|
534 |
+
"config": {
|
535 |
+
"model": "hf",
|
536 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
537 |
+
"batch_size": "auto",
|
538 |
+
"batch_sizes": [
|
539 |
+
64
|
540 |
+
],
|
541 |
+
"device": null,
|
542 |
+
"use_cache": null,
|
543 |
+
"limit": null,
|
544 |
+
"bootstrap_iters": 100000,
|
545 |
+
"gen_kwargs": null
|
546 |
+
},
|
547 |
+
"git_hash": "1ee41f7"
|
548 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:714bc81b1a9b797bb95c1606b76f2e5cc9f714e987136ff3c92e61a8a301a04b
|
3 |
+
size 96028
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,423 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"xstorycloze": {
|
4 |
+
"acc,none": 0.5785452138860477,
|
5 |
+
"acc_stderr,none": 0.046882211406773226,
|
6 |
+
"alias": "xstorycloze"
|
7 |
+
},
|
8 |
+
"xstorycloze_ar": {
|
9 |
+
"acc,none": 0.5373924553275976,
|
10 |
+
"acc_stderr,none": 0.012831093347016556,
|
11 |
+
"alias": " - xstorycloze_ar"
|
12 |
+
},
|
13 |
+
"xstorycloze_en": {
|
14 |
+
"acc,none": 0.7200529450694904,
|
15 |
+
"acc_stderr,none": 0.011553982180012723,
|
16 |
+
"alias": " - xstorycloze_en"
|
17 |
+
},
|
18 |
+
"xstorycloze_es": {
|
19 |
+
"acc,none": 0.6293845135671741,
|
20 |
+
"acc_stderr,none": 0.012428861084065901,
|
21 |
+
"alias": " - xstorycloze_es"
|
22 |
+
},
|
23 |
+
"xstorycloze_eu": {
|
24 |
+
"acc,none": 0.5334215751158173,
|
25 |
+
"acc_stderr,none": 0.01283834793473167,
|
26 |
+
"alias": " - xstorycloze_eu"
|
27 |
+
},
|
28 |
+
"xstorycloze_hi": {
|
29 |
+
"acc,none": 0.5407015221707479,
|
30 |
+
"acc_stderr,none": 0.012824422739625585,
|
31 |
+
"alias": " - xstorycloze_hi"
|
32 |
+
},
|
33 |
+
"xstorycloze_id": {
|
34 |
+
"acc,none": 0.614824619457313,
|
35 |
+
"acc_stderr,none": 0.012523231571141184,
|
36 |
+
"alias": " - xstorycloze_id"
|
37 |
+
},
|
38 |
+
"xstorycloze_my": {
|
39 |
+
"acc,none": 0.49172733289212445,
|
40 |
+
"acc_stderr,none": 0.012865364020375396,
|
41 |
+
"alias": " - xstorycloze_my"
|
42 |
+
},
|
43 |
+
"xstorycloze_ru": {
|
44 |
+
"acc,none": 0.6207809397749835,
|
45 |
+
"acc_stderr,none": 0.012486070771171334,
|
46 |
+
"alias": " - xstorycloze_ru"
|
47 |
+
},
|
48 |
+
"xstorycloze_sw": {
|
49 |
+
"acc,none": 0.5115817339510258,
|
50 |
+
"acc_stderr,none": 0.012863672949335879,
|
51 |
+
"alias": " - xstorycloze_sw"
|
52 |
+
},
|
53 |
+
"xstorycloze_te": {
|
54 |
+
"acc,none": 0.5691594970218399,
|
55 |
+
"acc_stderr,none": 0.012743443034698407,
|
56 |
+
"alias": " - xstorycloze_te"
|
57 |
+
},
|
58 |
+
"xstorycloze_zh": {
|
59 |
+
"acc,none": 0.5949702183984117,
|
60 |
+
"acc_stderr,none": 0.01263288721875138,
|
61 |
+
"alias": " - xstorycloze_zh"
|
62 |
+
}
|
63 |
+
},
|
64 |
+
"groups": {
|
65 |
+
"xstorycloze": {
|
66 |
+
"acc,none": 0.5785452138860477,
|
67 |
+
"acc_stderr,none": 0.046882211406773226,
|
68 |
+
"alias": "xstorycloze"
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"configs": {
|
72 |
+
"xstorycloze_ar": {
|
73 |
+
"task": "xstorycloze_ar",
|
74 |
+
"group": "xstorycloze",
|
75 |
+
"dataset_path": "juletxara/xstory_cloze",
|
76 |
+
"dataset_name": "ar",
|
77 |
+
"training_split": "train",
|
78 |
+
"validation_split": "eval",
|
79 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
80 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
81 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
82 |
+
"description": "",
|
83 |
+
"target_delimiter": " ",
|
84 |
+
"fewshot_delimiter": "\n\n",
|
85 |
+
"metric_list": [
|
86 |
+
{
|
87 |
+
"metric": "acc",
|
88 |
+
"aggregation": "mean",
|
89 |
+
"higher_is_better": true
|
90 |
+
}
|
91 |
+
],
|
92 |
+
"output_type": "multiple_choice",
|
93 |
+
"repeats": 1,
|
94 |
+
"should_decontaminate": true,
|
95 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
96 |
+
"metadata": {
|
97 |
+
"version": 1.0
|
98 |
+
}
|
99 |
+
},
|
100 |
+
"xstorycloze_en": {
|
101 |
+
"task": "xstorycloze_en",
|
102 |
+
"group": "xstorycloze",
|
103 |
+
"dataset_path": "juletxara/xstory_cloze",
|
104 |
+
"dataset_name": "en",
|
105 |
+
"training_split": "train",
|
106 |
+
"validation_split": "eval",
|
107 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
108 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
109 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
110 |
+
"description": "",
|
111 |
+
"target_delimiter": " ",
|
112 |
+
"fewshot_delimiter": "\n\n",
|
113 |
+
"metric_list": [
|
114 |
+
{
|
115 |
+
"metric": "acc",
|
116 |
+
"aggregation": "mean",
|
117 |
+
"higher_is_better": true
|
118 |
+
}
|
119 |
+
],
|
120 |
+
"output_type": "multiple_choice",
|
121 |
+
"repeats": 1,
|
122 |
+
"should_decontaminate": true,
|
123 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
124 |
+
"metadata": {
|
125 |
+
"version": 1.0
|
126 |
+
}
|
127 |
+
},
|
128 |
+
"xstorycloze_es": {
|
129 |
+
"task": "xstorycloze_es",
|
130 |
+
"group": "xstorycloze",
|
131 |
+
"dataset_path": "juletxara/xstory_cloze",
|
132 |
+
"dataset_name": "es",
|
133 |
+
"training_split": "train",
|
134 |
+
"validation_split": "eval",
|
135 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
136 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
137 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
138 |
+
"description": "",
|
139 |
+
"target_delimiter": " ",
|
140 |
+
"fewshot_delimiter": "\n\n",
|
141 |
+
"metric_list": [
|
142 |
+
{
|
143 |
+
"metric": "acc",
|
144 |
+
"aggregation": "mean",
|
145 |
+
"higher_is_better": true
|
146 |
+
}
|
147 |
+
],
|
148 |
+
"output_type": "multiple_choice",
|
149 |
+
"repeats": 1,
|
150 |
+
"should_decontaminate": true,
|
151 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
152 |
+
"metadata": {
|
153 |
+
"version": 1.0
|
154 |
+
}
|
155 |
+
},
|
156 |
+
"xstorycloze_eu": {
|
157 |
+
"task": "xstorycloze_eu",
|
158 |
+
"group": "xstorycloze",
|
159 |
+
"dataset_path": "juletxara/xstory_cloze",
|
160 |
+
"dataset_name": "eu",
|
161 |
+
"training_split": "train",
|
162 |
+
"validation_split": "eval",
|
163 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
164 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
165 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
166 |
+
"description": "",
|
167 |
+
"target_delimiter": " ",
|
168 |
+
"fewshot_delimiter": "\n\n",
|
169 |
+
"metric_list": [
|
170 |
+
{
|
171 |
+
"metric": "acc",
|
172 |
+
"aggregation": "mean",
|
173 |
+
"higher_is_better": true
|
174 |
+
}
|
175 |
+
],
|
176 |
+
"output_type": "multiple_choice",
|
177 |
+
"repeats": 1,
|
178 |
+
"should_decontaminate": true,
|
179 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
180 |
+
"metadata": {
|
181 |
+
"version": 1.0
|
182 |
+
}
|
183 |
+
},
|
184 |
+
"xstorycloze_hi": {
|
185 |
+
"task": "xstorycloze_hi",
|
186 |
+
"group": "xstorycloze",
|
187 |
+
"dataset_path": "juletxara/xstory_cloze",
|
188 |
+
"dataset_name": "hi",
|
189 |
+
"training_split": "train",
|
190 |
+
"validation_split": "eval",
|
191 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
192 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
193 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
194 |
+
"description": "",
|
195 |
+
"target_delimiter": " ",
|
196 |
+
"fewshot_delimiter": "\n\n",
|
197 |
+
"metric_list": [
|
198 |
+
{
|
199 |
+
"metric": "acc",
|
200 |
+
"aggregation": "mean",
|
201 |
+
"higher_is_better": true
|
202 |
+
}
|
203 |
+
],
|
204 |
+
"output_type": "multiple_choice",
|
205 |
+
"repeats": 1,
|
206 |
+
"should_decontaminate": true,
|
207 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
208 |
+
"metadata": {
|
209 |
+
"version": 1.0
|
210 |
+
}
|
211 |
+
},
|
212 |
+
"xstorycloze_id": {
|
213 |
+
"task": "xstorycloze_id",
|
214 |
+
"group": "xstorycloze",
|
215 |
+
"dataset_path": "juletxara/xstory_cloze",
|
216 |
+
"dataset_name": "id",
|
217 |
+
"training_split": "train",
|
218 |
+
"validation_split": "eval",
|
219 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
220 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
221 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
222 |
+
"description": "",
|
223 |
+
"target_delimiter": " ",
|
224 |
+
"fewshot_delimiter": "\n\n",
|
225 |
+
"metric_list": [
|
226 |
+
{
|
227 |
+
"metric": "acc",
|
228 |
+
"aggregation": "mean",
|
229 |
+
"higher_is_better": true
|
230 |
+
}
|
231 |
+
],
|
232 |
+
"output_type": "multiple_choice",
|
233 |
+
"repeats": 1,
|
234 |
+
"should_decontaminate": true,
|
235 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
236 |
+
"metadata": {
|
237 |
+
"version": 1.0
|
238 |
+
}
|
239 |
+
},
|
240 |
+
"xstorycloze_my": {
|
241 |
+
"task": "xstorycloze_my",
|
242 |
+
"group": "xstorycloze",
|
243 |
+
"dataset_path": "juletxara/xstory_cloze",
|
244 |
+
"dataset_name": "my",
|
245 |
+
"training_split": "train",
|
246 |
+
"validation_split": "eval",
|
247 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
248 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
249 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
250 |
+
"description": "",
|
251 |
+
"target_delimiter": " ",
|
252 |
+
"fewshot_delimiter": "\n\n",
|
253 |
+
"metric_list": [
|
254 |
+
{
|
255 |
+
"metric": "acc",
|
256 |
+
"aggregation": "mean",
|
257 |
+
"higher_is_better": true
|
258 |
+
}
|
259 |
+
],
|
260 |
+
"output_type": "multiple_choice",
|
261 |
+
"repeats": 1,
|
262 |
+
"should_decontaminate": true,
|
263 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
264 |
+
"metadata": {
|
265 |
+
"version": 1.0
|
266 |
+
}
|
267 |
+
},
|
268 |
+
"xstorycloze_ru": {
|
269 |
+
"task": "xstorycloze_ru",
|
270 |
+
"group": "xstorycloze",
|
271 |
+
"dataset_path": "juletxara/xstory_cloze",
|
272 |
+
"dataset_name": "ru",
|
273 |
+
"training_split": "train",
|
274 |
+
"validation_split": "eval",
|
275 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
276 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
277 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
278 |
+
"description": "",
|
279 |
+
"target_delimiter": " ",
|
280 |
+
"fewshot_delimiter": "\n\n",
|
281 |
+
"metric_list": [
|
282 |
+
{
|
283 |
+
"metric": "acc",
|
284 |
+
"aggregation": "mean",
|
285 |
+
"higher_is_better": true
|
286 |
+
}
|
287 |
+
],
|
288 |
+
"output_type": "multiple_choice",
|
289 |
+
"repeats": 1,
|
290 |
+
"should_decontaminate": true,
|
291 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
292 |
+
"metadata": {
|
293 |
+
"version": 1.0
|
294 |
+
}
|
295 |
+
},
|
296 |
+
"xstorycloze_sw": {
|
297 |
+
"task": "xstorycloze_sw",
|
298 |
+
"group": "xstorycloze",
|
299 |
+
"dataset_path": "juletxara/xstory_cloze",
|
300 |
+
"dataset_name": "sw",
|
301 |
+
"training_split": "train",
|
302 |
+
"validation_split": "eval",
|
303 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
304 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
305 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
306 |
+
"description": "",
|
307 |
+
"target_delimiter": " ",
|
308 |
+
"fewshot_delimiter": "\n\n",
|
309 |
+
"metric_list": [
|
310 |
+
{
|
311 |
+
"metric": "acc",
|
312 |
+
"aggregation": "mean",
|
313 |
+
"higher_is_better": true
|
314 |
+
}
|
315 |
+
],
|
316 |
+
"output_type": "multiple_choice",
|
317 |
+
"repeats": 1,
|
318 |
+
"should_decontaminate": true,
|
319 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
320 |
+
"metadata": {
|
321 |
+
"version": 1.0
|
322 |
+
}
|
323 |
+
},
|
324 |
+
"xstorycloze_te": {
|
325 |
+
"task": "xstorycloze_te",
|
326 |
+
"group": "xstorycloze",
|
327 |
+
"dataset_path": "juletxara/xstory_cloze",
|
328 |
+
"dataset_name": "te",
|
329 |
+
"training_split": "train",
|
330 |
+
"validation_split": "eval",
|
331 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
332 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
333 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
334 |
+
"description": "",
|
335 |
+
"target_delimiter": " ",
|
336 |
+
"fewshot_delimiter": "\n\n",
|
337 |
+
"metric_list": [
|
338 |
+
{
|
339 |
+
"metric": "acc",
|
340 |
+
"aggregation": "mean",
|
341 |
+
"higher_is_better": true
|
342 |
+
}
|
343 |
+
],
|
344 |
+
"output_type": "multiple_choice",
|
345 |
+
"repeats": 1,
|
346 |
+
"should_decontaminate": true,
|
347 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
348 |
+
"metadata": {
|
349 |
+
"version": 1.0
|
350 |
+
}
|
351 |
+
},
|
352 |
+
"xstorycloze_zh": {
|
353 |
+
"task": "xstorycloze_zh",
|
354 |
+
"group": "xstorycloze",
|
355 |
+
"dataset_path": "juletxara/xstory_cloze",
|
356 |
+
"dataset_name": "zh",
|
357 |
+
"training_split": "train",
|
358 |
+
"validation_split": "eval",
|
359 |
+
"doc_to_text": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
360 |
+
"doc_to_target": "{{answer_right_ending-1}}",
|
361 |
+
"doc_to_choice": "{{[sentence_quiz1, sentence_quiz2]}}",
|
362 |
+
"description": "",
|
363 |
+
"target_delimiter": " ",
|
364 |
+
"fewshot_delimiter": "\n\n",
|
365 |
+
"metric_list": [
|
366 |
+
{
|
367 |
+
"metric": "acc",
|
368 |
+
"aggregation": "mean",
|
369 |
+
"higher_is_better": true
|
370 |
+
}
|
371 |
+
],
|
372 |
+
"output_type": "multiple_choice",
|
373 |
+
"repeats": 1,
|
374 |
+
"should_decontaminate": true,
|
375 |
+
"doc_to_decontamination_query": "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}",
|
376 |
+
"metadata": {
|
377 |
+
"version": 1.0
|
378 |
+
}
|
379 |
+
}
|
380 |
+
},
|
381 |
+
"versions": {
|
382 |
+
"xstorycloze": "N/A",
|
383 |
+
"xstorycloze_ar": 1.0,
|
384 |
+
"xstorycloze_en": 1.0,
|
385 |
+
"xstorycloze_es": 1.0,
|
386 |
+
"xstorycloze_eu": 1.0,
|
387 |
+
"xstorycloze_hi": 1.0,
|
388 |
+
"xstorycloze_id": 1.0,
|
389 |
+
"xstorycloze_my": 1.0,
|
390 |
+
"xstorycloze_ru": 1.0,
|
391 |
+
"xstorycloze_sw": 1.0,
|
392 |
+
"xstorycloze_te": 1.0,
|
393 |
+
"xstorycloze_zh": 1.0
|
394 |
+
},
|
395 |
+
"n-shot": {
|
396 |
+
"xstorycloze": 0,
|
397 |
+
"xstorycloze_ar": 0,
|
398 |
+
"xstorycloze_en": 0,
|
399 |
+
"xstorycloze_es": 0,
|
400 |
+
"xstorycloze_eu": 0,
|
401 |
+
"xstorycloze_hi": 0,
|
402 |
+
"xstorycloze_id": 0,
|
403 |
+
"xstorycloze_my": 0,
|
404 |
+
"xstorycloze_ru": 0,
|
405 |
+
"xstorycloze_sw": 0,
|
406 |
+
"xstorycloze_te": 0,
|
407 |
+
"xstorycloze_zh": 0
|
408 |
+
},
|
409 |
+
"config": {
|
410 |
+
"model": "hf",
|
411 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
412 |
+
"batch_size": "auto",
|
413 |
+
"batch_sizes": [
|
414 |
+
64
|
415 |
+
],
|
416 |
+
"device": null,
|
417 |
+
"use_cache": null,
|
418 |
+
"limit": null,
|
419 |
+
"bootstrap_iters": 100000,
|
420 |
+
"gen_kwargs": null
|
421 |
+
},
|
422 |
+
"git_hash": "1ee41f7"
|
423 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:02c0a35886054983b3006e85f8ec40648b976eb0097ebdb4f54a1d47588b72b0
|
3 |
+
size 66269
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,248 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"xwinograd": {
|
4 |
+
"acc,none": 0.731175545066307,
|
5 |
+
"acc_stderr,none": 0.04568831187382474,
|
6 |
+
"alias": "xwinograd"
|
7 |
+
},
|
8 |
+
"xwinograd_en": {
|
9 |
+
"acc,none": 0.8094623655913978,
|
10 |
+
"acc_stderr,none": 0.008146492341553319,
|
11 |
+
"alias": " - xwinograd_en"
|
12 |
+
},
|
13 |
+
"xwinograd_fr": {
|
14 |
+
"acc,none": 0.7108433734939759,
|
15 |
+
"acc_stderr,none": 0.050066428050419214,
|
16 |
+
"alias": " - xwinograd_fr"
|
17 |
+
},
|
18 |
+
"xwinograd_jp": {
|
19 |
+
"acc,none": 0.6068821689259646,
|
20 |
+
"acc_stderr,none": 0.015780865040470965,
|
21 |
+
"alias": " - xwinograd_jp"
|
22 |
+
},
|
23 |
+
"xwinograd_pt": {
|
24 |
+
"acc,none": 0.6577946768060836,
|
25 |
+
"acc_stderr,none": 0.029311491114275143,
|
26 |
+
"alias": " - xwinograd_pt"
|
27 |
+
},
|
28 |
+
"xwinograd_ru": {
|
29 |
+
"acc,none": 0.6507936507936508,
|
30 |
+
"acc_stderr,none": 0.026902825537698707,
|
31 |
+
"alias": " - xwinograd_ru"
|
32 |
+
},
|
33 |
+
"xwinograd_zh": {
|
34 |
+
"acc,none": 0.6984126984126984,
|
35 |
+
"acc_stderr,none": 0.02046343784622378,
|
36 |
+
"alias": " - xwinograd_zh"
|
37 |
+
}
|
38 |
+
},
|
39 |
+
"groups": {
|
40 |
+
"xwinograd": {
|
41 |
+
"acc,none": 0.731175545066307,
|
42 |
+
"acc_stderr,none": 0.04568831187382474,
|
43 |
+
"alias": "xwinograd"
|
44 |
+
}
|
45 |
+
},
|
46 |
+
"configs": {
|
47 |
+
"xwinograd_en": {
|
48 |
+
"task": "xwinograd_en",
|
49 |
+
"group": [
|
50 |
+
"xwinograd"
|
51 |
+
],
|
52 |
+
"dataset_path": "Muennighoff/xwinograd",
|
53 |
+
"dataset_name": "en",
|
54 |
+
"test_split": "test",
|
55 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
56 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
57 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
58 |
+
"description": "",
|
59 |
+
"target_delimiter": " ",
|
60 |
+
"fewshot_delimiter": "\n\n",
|
61 |
+
"metric_list": [
|
62 |
+
{
|
63 |
+
"metric": "acc",
|
64 |
+
"aggregation": "mean",
|
65 |
+
"higher_is_better": true
|
66 |
+
}
|
67 |
+
],
|
68 |
+
"output_type": "multiple_choice",
|
69 |
+
"repeats": 1,
|
70 |
+
"should_decontaminate": false,
|
71 |
+
"metadata": {
|
72 |
+
"version": 1.0
|
73 |
+
}
|
74 |
+
},
|
75 |
+
"xwinograd_fr": {
|
76 |
+
"task": "xwinograd_fr",
|
77 |
+
"group": [
|
78 |
+
"xwinograd"
|
79 |
+
],
|
80 |
+
"dataset_path": "Muennighoff/xwinograd",
|
81 |
+
"dataset_name": "fr",
|
82 |
+
"test_split": "test",
|
83 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
84 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
85 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
86 |
+
"description": "",
|
87 |
+
"target_delimiter": " ",
|
88 |
+
"fewshot_delimiter": "\n\n",
|
89 |
+
"metric_list": [
|
90 |
+
{
|
91 |
+
"metric": "acc",
|
92 |
+
"aggregation": "mean",
|
93 |
+
"higher_is_better": true
|
94 |
+
}
|
95 |
+
],
|
96 |
+
"output_type": "multiple_choice",
|
97 |
+
"repeats": 1,
|
98 |
+
"should_decontaminate": false,
|
99 |
+
"metadata": {
|
100 |
+
"version": 1.0
|
101 |
+
}
|
102 |
+
},
|
103 |
+
"xwinograd_jp": {
|
104 |
+
"task": "xwinograd_jp",
|
105 |
+
"group": [
|
106 |
+
"xwinograd"
|
107 |
+
],
|
108 |
+
"dataset_path": "Muennighoff/xwinograd",
|
109 |
+
"dataset_name": "jp",
|
110 |
+
"test_split": "test",
|
111 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
112 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
113 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
114 |
+
"description": "",
|
115 |
+
"target_delimiter": " ",
|
116 |
+
"fewshot_delimiter": "\n\n",
|
117 |
+
"metric_list": [
|
118 |
+
{
|
119 |
+
"metric": "acc",
|
120 |
+
"aggregation": "mean",
|
121 |
+
"higher_is_better": true
|
122 |
+
}
|
123 |
+
],
|
124 |
+
"output_type": "multiple_choice",
|
125 |
+
"repeats": 1,
|
126 |
+
"should_decontaminate": false,
|
127 |
+
"metadata": {
|
128 |
+
"version": 1.0
|
129 |
+
}
|
130 |
+
},
|
131 |
+
"xwinograd_pt": {
|
132 |
+
"task": "xwinograd_pt",
|
133 |
+
"group": [
|
134 |
+
"xwinograd"
|
135 |
+
],
|
136 |
+
"dataset_path": "Muennighoff/xwinograd",
|
137 |
+
"dataset_name": "pt",
|
138 |
+
"test_split": "test",
|
139 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
140 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
141 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
142 |
+
"description": "",
|
143 |
+
"target_delimiter": " ",
|
144 |
+
"fewshot_delimiter": "\n\n",
|
145 |
+
"metric_list": [
|
146 |
+
{
|
147 |
+
"metric": "acc",
|
148 |
+
"aggregation": "mean",
|
149 |
+
"higher_is_better": true
|
150 |
+
}
|
151 |
+
],
|
152 |
+
"output_type": "multiple_choice",
|
153 |
+
"repeats": 1,
|
154 |
+
"should_decontaminate": false,
|
155 |
+
"metadata": {
|
156 |
+
"version": 1.0
|
157 |
+
}
|
158 |
+
},
|
159 |
+
"xwinograd_ru": {
|
160 |
+
"task": "xwinograd_ru",
|
161 |
+
"group": [
|
162 |
+
"xwinograd"
|
163 |
+
],
|
164 |
+
"dataset_path": "Muennighoff/xwinograd",
|
165 |
+
"dataset_name": "ru",
|
166 |
+
"test_split": "test",
|
167 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
168 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
169 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
170 |
+
"description": "",
|
171 |
+
"target_delimiter": " ",
|
172 |
+
"fewshot_delimiter": "\n\n",
|
173 |
+
"metric_list": [
|
174 |
+
{
|
175 |
+
"metric": "acc",
|
176 |
+
"aggregation": "mean",
|
177 |
+
"higher_is_better": true
|
178 |
+
}
|
179 |
+
],
|
180 |
+
"output_type": "multiple_choice",
|
181 |
+
"repeats": 1,
|
182 |
+
"should_decontaminate": false,
|
183 |
+
"metadata": {
|
184 |
+
"version": 1.0
|
185 |
+
}
|
186 |
+
},
|
187 |
+
"xwinograd_zh": {
|
188 |
+
"task": "xwinograd_zh",
|
189 |
+
"group": [
|
190 |
+
"xwinograd"
|
191 |
+
],
|
192 |
+
"dataset_path": "Muennighoff/xwinograd",
|
193 |
+
"dataset_name": "zh",
|
194 |
+
"test_split": "test",
|
195 |
+
"doc_to_text": "def doc_to_text(doc: Dict) -> int:\n \"\"\"\n Return index of the correct choice.\n\n Note: We are using the \"multiple input\" mode of the multiple-choice\n output-type, which means we use different contexts with the same target\n for the different choices, rather than the same context and different targets.\n \"\"\"\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
196 |
+
"doc_to_target": "def doc_to_target(doc: Dict) -> str:\n \"\"\"\n Return the target completion.\n\n Note that this does not depend on the correct choice as we are using\n \"multiple input\" mode.\n \"\"\"\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
197 |
+
"doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
198 |
+
"description": "",
|
199 |
+
"target_delimiter": " ",
|
200 |
+
"fewshot_delimiter": "\n\n",
|
201 |
+
"metric_list": [
|
202 |
+
{
|
203 |
+
"metric": "acc",
|
204 |
+
"aggregation": "mean",
|
205 |
+
"higher_is_better": true
|
206 |
+
}
|
207 |
+
],
|
208 |
+
"output_type": "multiple_choice",
|
209 |
+
"repeats": 1,
|
210 |
+
"should_decontaminate": false,
|
211 |
+
"metadata": {
|
212 |
+
"version": 1.0
|
213 |
+
}
|
214 |
+
}
|
215 |
+
},
|
216 |
+
"versions": {
|
217 |
+
"xwinograd": "N/A",
|
218 |
+
"xwinograd_en": 1.0,
|
219 |
+
"xwinograd_fr": 1.0,
|
220 |
+
"xwinograd_jp": 1.0,
|
221 |
+
"xwinograd_pt": 1.0,
|
222 |
+
"xwinograd_ru": 1.0,
|
223 |
+
"xwinograd_zh": 1.0
|
224 |
+
},
|
225 |
+
"n-shot": {
|
226 |
+
"xwinograd": 0,
|
227 |
+
"xwinograd_en": 0,
|
228 |
+
"xwinograd_fr": 0,
|
229 |
+
"xwinograd_jp": 0,
|
230 |
+
"xwinograd_pt": 0,
|
231 |
+
"xwinograd_ru": 0,
|
232 |
+
"xwinograd_zh": 0
|
233 |
+
},
|
234 |
+
"config": {
|
235 |
+
"model": "hf",
|
236 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-1b5,dtype=bfloat16,trust_remote_code=True",
|
237 |
+
"batch_size": "auto",
|
238 |
+
"batch_sizes": [
|
239 |
+
64
|
240 |
+
],
|
241 |
+
"device": null,
|
242 |
+
"use_cache": null,
|
243 |
+
"limit": null,
|
244 |
+
"bootstrap_iters": 100000,
|
245 |
+
"gen_kwargs": null
|
246 |
+
},
|
247 |
+
"git_hash": "1ee41f7"
|
248 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-1b5/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0cca47945ffd0b9dfffb5f897ba2af5cbc65a3f2b722ed9d9889eb25c470e80a
|
3 |
+
size 60297
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"ai2_arc": {
|
4 |
+
"acc,none": 0.5727170236753101,
|
5 |
+
"acc_stderr,none": 0.10942748330722392,
|
6 |
+
"acc_norm,none": 0.547914317925592,
|
7 |
+
"acc_norm_stderr,none": 0.08710699872372187,
|
8 |
+
"alias": "ai2_arc"
|
9 |
+
},
|
10 |
+
"arc_challenge": {
|
11 |
+
"acc,none": 0.3412969283276451,
|
12 |
+
"acc_stderr,none": 0.013855831287497728,
|
13 |
+
"acc_norm,none": 0.3643344709897611,
|
14 |
+
"acc_norm_stderr,none": 0.014063260279882413,
|
15 |
+
"alias": " - arc_challenge"
|
16 |
+
},
|
17 |
+
"arc_easy": {
|
18 |
+
"acc,none": 0.6868686868686869,
|
19 |
+
"acc_stderr,none": 0.00951630387930954,
|
20 |
+
"acc_norm,none": 0.6384680134680135,
|
21 |
+
"acc_norm_stderr,none": 0.00985850654316206,
|
22 |
+
"alias": " - arc_easy"
|
23 |
+
}
|
24 |
+
},
|
25 |
+
"groups": {
|
26 |
+
"ai2_arc": {
|
27 |
+
"acc,none": 0.5727170236753101,
|
28 |
+
"acc_stderr,none": 0.10942748330722392,
|
29 |
+
"acc_norm,none": 0.547914317925592,
|
30 |
+
"acc_norm_stderr,none": 0.08710699872372187,
|
31 |
+
"alias": "ai2_arc"
|
32 |
+
}
|
33 |
+
},
|
34 |
+
"configs": {
|
35 |
+
"arc_challenge": {
|
36 |
+
"task": "arc_challenge",
|
37 |
+
"group": [
|
38 |
+
"ai2_arc"
|
39 |
+
],
|
40 |
+
"dataset_path": "allenai/ai2_arc",
|
41 |
+
"dataset_name": "ARC-Challenge",
|
42 |
+
"training_split": "train",
|
43 |
+
"validation_split": "validation",
|
44 |
+
"test_split": "test",
|
45 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
46 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
47 |
+
"doc_to_choice": "{{choices.text}}",
|
48 |
+
"description": "",
|
49 |
+
"target_delimiter": " ",
|
50 |
+
"fewshot_delimiter": "\n\n",
|
51 |
+
"metric_list": [
|
52 |
+
{
|
53 |
+
"metric": "acc",
|
54 |
+
"aggregation": "mean",
|
55 |
+
"higher_is_better": true
|
56 |
+
},
|
57 |
+
{
|
58 |
+
"metric": "acc_norm",
|
59 |
+
"aggregation": "mean",
|
60 |
+
"higher_is_better": true
|
61 |
+
}
|
62 |
+
],
|
63 |
+
"output_type": "multiple_choice",
|
64 |
+
"repeats": 1,
|
65 |
+
"should_decontaminate": true,
|
66 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
67 |
+
"metadata": {
|
68 |
+
"version": 1.0
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"arc_easy": {
|
72 |
+
"task": "arc_easy",
|
73 |
+
"group": [
|
74 |
+
"ai2_arc"
|
75 |
+
],
|
76 |
+
"dataset_path": "allenai/ai2_arc",
|
77 |
+
"dataset_name": "ARC-Easy",
|
78 |
+
"training_split": "train",
|
79 |
+
"validation_split": "validation",
|
80 |
+
"test_split": "test",
|
81 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
82 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
83 |
+
"doc_to_choice": "{{choices.text}}",
|
84 |
+
"description": "",
|
85 |
+
"target_delimiter": " ",
|
86 |
+
"fewshot_delimiter": "\n\n",
|
87 |
+
"metric_list": [
|
88 |
+
{
|
89 |
+
"metric": "acc",
|
90 |
+
"aggregation": "mean",
|
91 |
+
"higher_is_better": true
|
92 |
+
},
|
93 |
+
{
|
94 |
+
"metric": "acc_norm",
|
95 |
+
"aggregation": "mean",
|
96 |
+
"higher_is_better": true
|
97 |
+
}
|
98 |
+
],
|
99 |
+
"output_type": "multiple_choice",
|
100 |
+
"repeats": 1,
|
101 |
+
"should_decontaminate": true,
|
102 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
103 |
+
"metadata": {
|
104 |
+
"version": 1.0
|
105 |
+
}
|
106 |
+
}
|
107 |
+
},
|
108 |
+
"versions": {
|
109 |
+
"ai2_arc": "N/A",
|
110 |
+
"arc_challenge": 1.0,
|
111 |
+
"arc_easy": 1.0
|
112 |
+
},
|
113 |
+
"n-shot": {
|
114 |
+
"ai2_arc": 0,
|
115 |
+
"arc_challenge": 0,
|
116 |
+
"arc_easy": 0
|
117 |
+
},
|
118 |
+
"config": {
|
119 |
+
"model": "hf",
|
120 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-3b,dtype=bfloat16,trust_remote_code=True",
|
121 |
+
"batch_size": "auto",
|
122 |
+
"batch_sizes": [
|
123 |
+
64
|
124 |
+
],
|
125 |
+
"device": null,
|
126 |
+
"use_cache": null,
|
127 |
+
"limit": null,
|
128 |
+
"bootstrap_iters": 100000,
|
129 |
+
"gen_kwargs": null
|
130 |
+
},
|
131 |
+
"git_hash": "1ee41f7"
|
132 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:01683ce8c83a5edff112e6bc70781594ed00d27b6d09eff636ff46d4b8cb678b
|
3 |
+
size 47723
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,161 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"anli": {
|
4 |
+
"acc,none": 0.34375,
|
5 |
+
"acc_stderr,none": 0.01498089438146567,
|
6 |
+
"alias": "anli"
|
7 |
+
},
|
8 |
+
"anli_r1": {
|
9 |
+
"acc,none": 0.346,
|
10 |
+
"acc_stderr,none": 0.015050266127564436,
|
11 |
+
"alias": " - anli_r1"
|
12 |
+
},
|
13 |
+
"anli_r2": {
|
14 |
+
"acc,none": 0.351,
|
15 |
+
"acc_stderr,none": 0.015100563798316407,
|
16 |
+
"alias": " - anli_r2"
|
17 |
+
},
|
18 |
+
"anli_r3": {
|
19 |
+
"acc,none": 0.3358333333333333,
|
20 |
+
"acc_stderr,none": 0.013639261190932879,
|
21 |
+
"alias": " - anli_r3"
|
22 |
+
}
|
23 |
+
},
|
24 |
+
"groups": {
|
25 |
+
"anli": {
|
26 |
+
"acc,none": 0.34375,
|
27 |
+
"acc_stderr,none": 0.01498089438146567,
|
28 |
+
"alias": "anli"
|
29 |
+
}
|
30 |
+
},
|
31 |
+
"configs": {
|
32 |
+
"anli_r1": {
|
33 |
+
"task": "anli_r1",
|
34 |
+
"group": [
|
35 |
+
"anli"
|
36 |
+
],
|
37 |
+
"dataset_path": "anli",
|
38 |
+
"training_split": "train_r1",
|
39 |
+
"validation_split": "dev_r1",
|
40 |
+
"test_split": "test_r1",
|
41 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
42 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
43 |
+
"doc_to_choice": [
|
44 |
+
"True",
|
45 |
+
"Neither",
|
46 |
+
"False"
|
47 |
+
],
|
48 |
+
"description": "",
|
49 |
+
"target_delimiter": " ",
|
50 |
+
"fewshot_delimiter": "\n\n",
|
51 |
+
"metric_list": [
|
52 |
+
{
|
53 |
+
"metric": "acc",
|
54 |
+
"aggregation": "mean",
|
55 |
+
"higher_is_better": true
|
56 |
+
}
|
57 |
+
],
|
58 |
+
"output_type": "multiple_choice",
|
59 |
+
"repeats": 1,
|
60 |
+
"should_decontaminate": true,
|
61 |
+
"doc_to_decontamination_query": "premise",
|
62 |
+
"metadata": {
|
63 |
+
"version": 1.0
|
64 |
+
}
|
65 |
+
},
|
66 |
+
"anli_r2": {
|
67 |
+
"task": "anli_r2",
|
68 |
+
"group": [
|
69 |
+
"anli"
|
70 |
+
],
|
71 |
+
"dataset_path": "anli",
|
72 |
+
"training_split": "train_r2",
|
73 |
+
"validation_split": "dev_r2",
|
74 |
+
"test_split": "test_r2",
|
75 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
76 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
77 |
+
"doc_to_choice": [
|
78 |
+
"True",
|
79 |
+
"Neither",
|
80 |
+
"False"
|
81 |
+
],
|
82 |
+
"description": "",
|
83 |
+
"target_delimiter": " ",
|
84 |
+
"fewshot_delimiter": "\n\n",
|
85 |
+
"metric_list": [
|
86 |
+
{
|
87 |
+
"metric": "acc",
|
88 |
+
"aggregation": "mean",
|
89 |
+
"higher_is_better": true
|
90 |
+
}
|
91 |
+
],
|
92 |
+
"output_type": "multiple_choice",
|
93 |
+
"repeats": 1,
|
94 |
+
"should_decontaminate": true,
|
95 |
+
"doc_to_decontamination_query": "premise",
|
96 |
+
"metadata": {
|
97 |
+
"version": 1.0
|
98 |
+
}
|
99 |
+
},
|
100 |
+
"anli_r3": {
|
101 |
+
"task": "anli_r3",
|
102 |
+
"group": [
|
103 |
+
"anli"
|
104 |
+
],
|
105 |
+
"dataset_path": "anli",
|
106 |
+
"training_split": "train_r3",
|
107 |
+
"validation_split": "dev_r3",
|
108 |
+
"test_split": "test_r3",
|
109 |
+
"doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
|
110 |
+
"doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
|
111 |
+
"doc_to_choice": [
|
112 |
+
"True",
|
113 |
+
"Neither",
|
114 |
+
"False"
|
115 |
+
],
|
116 |
+
"description": "",
|
117 |
+
"target_delimiter": " ",
|
118 |
+
"fewshot_delimiter": "\n\n",
|
119 |
+
"metric_list": [
|
120 |
+
{
|
121 |
+
"metric": "acc",
|
122 |
+
"aggregation": "mean",
|
123 |
+
"higher_is_better": true
|
124 |
+
}
|
125 |
+
],
|
126 |
+
"output_type": "multiple_choice",
|
127 |
+
"repeats": 1,
|
128 |
+
"should_decontaminate": true,
|
129 |
+
"doc_to_decontamination_query": "premise",
|
130 |
+
"metadata": {
|
131 |
+
"version": 1.0
|
132 |
+
}
|
133 |
+
}
|
134 |
+
},
|
135 |
+
"versions": {
|
136 |
+
"anli": "N/A",
|
137 |
+
"anli_r1": 1.0,
|
138 |
+
"anli_r2": 1.0,
|
139 |
+
"anli_r3": 1.0
|
140 |
+
},
|
141 |
+
"n-shot": {
|
142 |
+
"anli": 0,
|
143 |
+
"anli_r1": 0,
|
144 |
+
"anli_r2": 0,
|
145 |
+
"anli_r3": 0
|
146 |
+
},
|
147 |
+
"config": {
|
148 |
+
"model": "hf",
|
149 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-3b,dtype=bfloat16,trust_remote_code=True",
|
150 |
+
"batch_size": "auto",
|
151 |
+
"batch_sizes": [
|
152 |
+
64
|
153 |
+
],
|
154 |
+
"device": null,
|
155 |
+
"use_cache": null,
|
156 |
+
"limit": null,
|
157 |
+
"bootstrap_iters": 100000,
|
158 |
+
"gen_kwargs": null
|
159 |
+
},
|
160 |
+
"git_hash": "1ee41f7"
|
161 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9c7f6f7ac954cae5ee247cabc31835d6835e1dd702a1eeda34bced4dc7cede4a
|
3 |
+
size 69276
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,2249 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"blimp": {
|
4 |
+
"acc,none": 0.8394328358208956,
|
5 |
+
"acc_stderr,none": 0.13653720128092459,
|
6 |
+
"alias": "blimp"
|
7 |
+
},
|
8 |
+
"blimp_adjunct_island": {
|
9 |
+
"acc,none": 0.909,
|
10 |
+
"acc_stderr,none": 0.009099549538400236,
|
11 |
+
"alias": " - blimp_adjunct_island"
|
12 |
+
},
|
13 |
+
"blimp_anaphor_gender_agreement": {
|
14 |
+
"acc,none": 0.986,
|
15 |
+
"acc_stderr,none": 0.003717232548256562,
|
16 |
+
"alias": " - blimp_anaphor_gender_agreement"
|
17 |
+
},
|
18 |
+
"blimp_anaphor_number_agreement": {
|
19 |
+
"acc,none": 0.994,
|
20 |
+
"acc_stderr,none": 0.00244335219932984,
|
21 |
+
"alias": " - blimp_anaphor_number_agreement"
|
22 |
+
},
|
23 |
+
"blimp_animate_subject_passive": {
|
24 |
+
"acc,none": 0.804,
|
25 |
+
"acc_stderr,none": 0.012559527926707363,
|
26 |
+
"alias": " - blimp_animate_subject_passive"
|
27 |
+
},
|
28 |
+
"blimp_animate_subject_trans": {
|
29 |
+
"acc,none": 0.89,
|
30 |
+
"acc_stderr,none": 0.009899393819724447,
|
31 |
+
"alias": " - blimp_animate_subject_trans"
|
32 |
+
},
|
33 |
+
"blimp_causative": {
|
34 |
+
"acc,none": 0.765,
|
35 |
+
"acc_stderr,none": 0.01341472903024711,
|
36 |
+
"alias": " - blimp_causative"
|
37 |
+
},
|
38 |
+
"blimp_complex_NP_island": {
|
39 |
+
"acc,none": 0.707,
|
40 |
+
"acc_stderr,none": 0.014399942998441275,
|
41 |
+
"alias": " - blimp_complex_NP_island"
|
42 |
+
},
|
43 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
44 |
+
"acc,none": 0.697,
|
45 |
+
"acc_stderr,none": 0.01453968371053524,
|
46 |
+
"alias": " - blimp_coordinate_structure_constraint_complex_left_branch"
|
47 |
+
},
|
48 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
49 |
+
"acc,none": 0.868,
|
50 |
+
"acc_stderr,none": 0.010709373963528022,
|
51 |
+
"alias": " - blimp_coordinate_structure_constraint_object_extraction"
|
52 |
+
},
|
53 |
+
"blimp_determiner_noun_agreement_1": {
|
54 |
+
"acc,none": 0.99,
|
55 |
+
"acc_stderr,none": 0.0031480009386767667,
|
56 |
+
"alias": " - blimp_determiner_noun_agreement_1"
|
57 |
+
},
|
58 |
+
"blimp_determiner_noun_agreement_2": {
|
59 |
+
"acc,none": 0.984,
|
60 |
+
"acc_stderr,none": 0.003969856390319422,
|
61 |
+
"alias": " - blimp_determiner_noun_agreement_2"
|
62 |
+
},
|
63 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
64 |
+
"acc,none": 0.933,
|
65 |
+
"acc_stderr,none": 0.007910345983177549,
|
66 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_1"
|
67 |
+
},
|
68 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
69 |
+
"acc,none": 0.934,
|
70 |
+
"acc_stderr,none": 0.007855297938697596,
|
71 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_2"
|
72 |
+
},
|
73 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
74 |
+
"acc,none": 0.964,
|
75 |
+
"acc_stderr,none": 0.005893957816165557,
|
76 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_2"
|
77 |
+
},
|
78 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
79 |
+
"acc,none": 0.915,
|
80 |
+
"acc_stderr,none": 0.008823426366942302,
|
81 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_1"
|
82 |
+
},
|
83 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
84 |
+
"acc,none": 0.93,
|
85 |
+
"acc_stderr,none": 0.00807249435832349,
|
86 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_2"
|
87 |
+
},
|
88 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
89 |
+
"acc,none": 0.98,
|
90 |
+
"acc_stderr,none": 0.004429403980178342,
|
91 |
+
"alias": " - blimp_determiner_noun_agreement_with_adjective_1"
|
92 |
+
},
|
93 |
+
"blimp_distractor_agreement_relational_noun": {
|
94 |
+
"acc,none": 0.884,
|
95 |
+
"acc_stderr,none": 0.01013146813875699,
|
96 |
+
"alias": " - blimp_distractor_agreement_relational_noun"
|
97 |
+
},
|
98 |
+
"blimp_distractor_agreement_relative_clause": {
|
99 |
+
"acc,none": 0.762,
|
100 |
+
"acc_stderr,none": 0.01347358666196722,
|
101 |
+
"alias": " - blimp_distractor_agreement_relative_clause"
|
102 |
+
},
|
103 |
+
"blimp_drop_argument": {
|
104 |
+
"acc,none": 0.814,
|
105 |
+
"acc_stderr,none": 0.012310790208412805,
|
106 |
+
"alias": " - blimp_drop_argument"
|
107 |
+
},
|
108 |
+
"blimp_ellipsis_n_bar_1": {
|
109 |
+
"acc,none": 0.852,
|
110 |
+
"acc_stderr,none": 0.011234866364235253,
|
111 |
+
"alias": " - blimp_ellipsis_n_bar_1"
|
112 |
+
},
|
113 |
+
"blimp_ellipsis_n_bar_2": {
|
114 |
+
"acc,none": 0.912,
|
115 |
+
"acc_stderr,none": 0.00896305396259208,
|
116 |
+
"alias": " - blimp_ellipsis_n_bar_2"
|
117 |
+
},
|
118 |
+
"blimp_existential_there_object_raising": {
|
119 |
+
"acc,none": 0.859,
|
120 |
+
"acc_stderr,none": 0.011010914595992441,
|
121 |
+
"alias": " - blimp_existential_there_object_raising"
|
122 |
+
},
|
123 |
+
"blimp_existential_there_quantifiers_1": {
|
124 |
+
"acc,none": 0.994,
|
125 |
+
"acc_stderr,none": 0.0024433521993298185,
|
126 |
+
"alias": " - blimp_existential_there_quantifiers_1"
|
127 |
+
},
|
128 |
+
"blimp_existential_there_quantifiers_2": {
|
129 |
+
"acc,none": 0.457,
|
130 |
+
"acc_stderr,none": 0.01576069159013639,
|
131 |
+
"alias": " - blimp_existential_there_quantifiers_2"
|
132 |
+
},
|
133 |
+
"blimp_existential_there_subject_raising": {
|
134 |
+
"acc,none": 0.905,
|
135 |
+
"acc_stderr,none": 0.009276910103103315,
|
136 |
+
"alias": " - blimp_existential_there_subject_raising"
|
137 |
+
},
|
138 |
+
"blimp_expletive_it_object_raising": {
|
139 |
+
"acc,none": 0.805,
|
140 |
+
"acc_stderr,none": 0.012535235623319327,
|
141 |
+
"alias": " - blimp_expletive_it_object_raising"
|
142 |
+
},
|
143 |
+
"blimp_inchoative": {
|
144 |
+
"acc,none": 0.743,
|
145 |
+
"acc_stderr,none": 0.013825416526895031,
|
146 |
+
"alias": " - blimp_inchoative"
|
147 |
+
},
|
148 |
+
"blimp_intransitive": {
|
149 |
+
"acc,none": 0.843,
|
150 |
+
"acc_stderr,none": 0.011510146979230189,
|
151 |
+
"alias": " - blimp_intransitive"
|
152 |
+
},
|
153 |
+
"blimp_irregular_past_participle_adjectives": {
|
154 |
+
"acc,none": 0.941,
|
155 |
+
"acc_stderr,none": 0.007454835650406725,
|
156 |
+
"alias": " - blimp_irregular_past_participle_adjectives"
|
157 |
+
},
|
158 |
+
"blimp_irregular_past_participle_verbs": {
|
159 |
+
"acc,none": 0.927,
|
160 |
+
"acc_stderr,none": 0.008230354715244075,
|
161 |
+
"alias": " - blimp_irregular_past_participle_verbs"
|
162 |
+
},
|
163 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
164 |
+
"acc,none": 0.932,
|
165 |
+
"acc_stderr,none": 0.007964887911291605,
|
166 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_1"
|
167 |
+
},
|
168 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
169 |
+
"acc,none": 0.926,
|
170 |
+
"acc_stderr,none": 0.00828206451270415,
|
171 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_2"
|
172 |
+
},
|
173 |
+
"blimp_left_branch_island_echo_question": {
|
174 |
+
"acc,none": 0.598,
|
175 |
+
"acc_stderr,none": 0.015512467135715075,
|
176 |
+
"alias": " - blimp_left_branch_island_echo_question"
|
177 |
+
},
|
178 |
+
"blimp_left_branch_island_simple_question": {
|
179 |
+
"acc,none": 0.834,
|
180 |
+
"acc_stderr,none": 0.011772110370812203,
|
181 |
+
"alias": " - blimp_left_branch_island_simple_question"
|
182 |
+
},
|
183 |
+
"blimp_matrix_question_npi_licensor_present": {
|
184 |
+
"acc,none": 0.581,
|
185 |
+
"acc_stderr,none": 0.015610338967577794,
|
186 |
+
"alias": " - blimp_matrix_question_npi_licensor_present"
|
187 |
+
},
|
188 |
+
"blimp_npi_present_1": {
|
189 |
+
"acc,none": 0.626,
|
190 |
+
"acc_stderr,none": 0.015308767369006366,
|
191 |
+
"alias": " - blimp_npi_present_1"
|
192 |
+
},
|
193 |
+
"blimp_npi_present_2": {
|
194 |
+
"acc,none": 0.716,
|
195 |
+
"acc_stderr,none": 0.014267009061031309,
|
196 |
+
"alias": " - blimp_npi_present_2"
|
197 |
+
},
|
198 |
+
"blimp_only_npi_licensor_present": {
|
199 |
+
"acc,none": 0.866,
|
200 |
+
"acc_stderr,none": 0.010777762298369686,
|
201 |
+
"alias": " - blimp_only_npi_licensor_present"
|
202 |
+
},
|
203 |
+
"blimp_only_npi_scope": {
|
204 |
+
"acc,none": 0.817,
|
205 |
+
"acc_stderr,none": 0.012233587399477823,
|
206 |
+
"alias": " - blimp_only_npi_scope"
|
207 |
+
},
|
208 |
+
"blimp_passive_1": {
|
209 |
+
"acc,none": 0.895,
|
210 |
+
"acc_stderr,none": 0.009698921026024966,
|
211 |
+
"alias": " - blimp_passive_1"
|
212 |
+
},
|
213 |
+
"blimp_passive_2": {
|
214 |
+
"acc,none": 0.905,
|
215 |
+
"acc_stderr,none": 0.009276910103103319,
|
216 |
+
"alias": " - blimp_passive_2"
|
217 |
+
},
|
218 |
+
"blimp_principle_A_c_command": {
|
219 |
+
"acc,none": 0.763,
|
220 |
+
"acc_stderr,none": 0.013454070462577943,
|
221 |
+
"alias": " - blimp_principle_A_c_command"
|
222 |
+
},
|
223 |
+
"blimp_principle_A_case_1": {
|
224 |
+
"acc,none": 1.0,
|
225 |
+
"acc_stderr,none": 0.0,
|
226 |
+
"alias": " - blimp_principle_A_case_1"
|
227 |
+
},
|
228 |
+
"blimp_principle_A_case_2": {
|
229 |
+
"acc,none": 0.975,
|
230 |
+
"acc_stderr,none": 0.004939574819698464,
|
231 |
+
"alias": " - blimp_principle_A_case_2"
|
232 |
+
},
|
233 |
+
"blimp_principle_A_domain_1": {
|
234 |
+
"acc,none": 0.997,
|
235 |
+
"acc_stderr,none": 0.0017303161543469417,
|
236 |
+
"alias": " - blimp_principle_A_domain_1"
|
237 |
+
},
|
238 |
+
"blimp_principle_A_domain_2": {
|
239 |
+
"acc,none": 0.914,
|
240 |
+
"acc_stderr,none": 0.008870325962594766,
|
241 |
+
"alias": " - blimp_principle_A_domain_2"
|
242 |
+
},
|
243 |
+
"blimp_principle_A_domain_3": {
|
244 |
+
"acc,none": 0.852,
|
245 |
+
"acc_stderr,none": 0.01123486636423525,
|
246 |
+
"alias": " - blimp_principle_A_domain_3"
|
247 |
+
},
|
248 |
+
"blimp_principle_A_reconstruction": {
|
249 |
+
"acc,none": 0.468,
|
250 |
+
"acc_stderr,none": 0.01578686875935901,
|
251 |
+
"alias": " - blimp_principle_A_reconstruction"
|
252 |
+
},
|
253 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
254 |
+
"acc,none": 0.968,
|
255 |
+
"acc_stderr,none": 0.005568393575081361,
|
256 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_1"
|
257 |
+
},
|
258 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
259 |
+
"acc,none": 0.931,
|
260 |
+
"acc_stderr,none": 0.008018934050315162,
|
261 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_2"
|
262 |
+
},
|
263 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
264 |
+
"acc,none": 0.973,
|
265 |
+
"acc_stderr,none": 0.005128089049275288,
|
266 |
+
"alias": " - blimp_sentential_negation_npi_licensor_present"
|
267 |
+
},
|
268 |
+
"blimp_sentential_negation_npi_scope": {
|
269 |
+
"acc,none": 0.792,
|
270 |
+
"acc_stderr,none": 0.01284137457209692,
|
271 |
+
"alias": " - blimp_sentential_negation_npi_scope"
|
272 |
+
},
|
273 |
+
"blimp_sentential_subject_island": {
|
274 |
+
"acc,none": 0.483,
|
275 |
+
"acc_stderr,none": 0.01581015372983343,
|
276 |
+
"alias": " - blimp_sentential_subject_island"
|
277 |
+
},
|
278 |
+
"blimp_superlative_quantifiers_1": {
|
279 |
+
"acc,none": 0.862,
|
280 |
+
"acc_stderr,none": 0.010912152632504417,
|
281 |
+
"alias": " - blimp_superlative_quantifiers_1"
|
282 |
+
},
|
283 |
+
"blimp_superlative_quantifiers_2": {
|
284 |
+
"acc,none": 0.923,
|
285 |
+
"acc_stderr,none": 0.00843458014024062,
|
286 |
+
"alias": " - blimp_superlative_quantifiers_2"
|
287 |
+
},
|
288 |
+
"blimp_tough_vs_raising_1": {
|
289 |
+
"acc,none": 0.691,
|
290 |
+
"acc_stderr,none": 0.014619600977206493,
|
291 |
+
"alias": " - blimp_tough_vs_raising_1"
|
292 |
+
},
|
293 |
+
"blimp_tough_vs_raising_2": {
|
294 |
+
"acc,none": 0.885,
|
295 |
+
"acc_stderr,none": 0.010093407594904605,
|
296 |
+
"alias": " - blimp_tough_vs_raising_2"
|
297 |
+
},
|
298 |
+
"blimp_transitive": {
|
299 |
+
"acc,none": 0.898,
|
300 |
+
"acc_stderr,none": 0.009575368801653876,
|
301 |
+
"alias": " - blimp_transitive"
|
302 |
+
},
|
303 |
+
"blimp_wh_island": {
|
304 |
+
"acc,none": 0.766,
|
305 |
+
"acc_stderr,none": 0.013394902889660009,
|
306 |
+
"alias": " - blimp_wh_island"
|
307 |
+
},
|
308 |
+
"blimp_wh_questions_object_gap": {
|
309 |
+
"acc,none": 0.841,
|
310 |
+
"acc_stderr,none": 0.011569479368271306,
|
311 |
+
"alias": " - blimp_wh_questions_object_gap"
|
312 |
+
},
|
313 |
+
"blimp_wh_questions_subject_gap": {
|
314 |
+
"acc,none": 0.956,
|
315 |
+
"acc_stderr,none": 0.00648892179842742,
|
316 |
+
"alias": " - blimp_wh_questions_subject_gap"
|
317 |
+
},
|
318 |
+
"blimp_wh_questions_subject_gap_long_distance": {
|
319 |
+
"acc,none": 0.936,
|
320 |
+
"acc_stderr,none": 0.007743640226919304,
|
321 |
+
"alias": " - blimp_wh_questions_subject_gap_long_distance"
|
322 |
+
},
|
323 |
+
"blimp_wh_vs_that_no_gap": {
|
324 |
+
"acc,none": 0.975,
|
325 |
+
"acc_stderr,none": 0.004939574819698465,
|
326 |
+
"alias": " - blimp_wh_vs_that_no_gap"
|
327 |
+
},
|
328 |
+
"blimp_wh_vs_that_no_gap_long_distance": {
|
329 |
+
"acc,none": 0.97,
|
330 |
+
"acc_stderr,none": 0.005397140829099214,
|
331 |
+
"alias": " - blimp_wh_vs_that_no_gap_long_distance"
|
332 |
+
},
|
333 |
+
"blimp_wh_vs_that_with_gap": {
|
334 |
+
"acc,none": 0.438,
|
335 |
+
"acc_stderr,none": 0.01569721001969469,
|
336 |
+
"alias": " - blimp_wh_vs_that_with_gap"
|
337 |
+
},
|
338 |
+
"blimp_wh_vs_that_with_gap_long_distance": {
|
339 |
+
"acc,none": 0.341,
|
340 |
+
"acc_stderr,none": 0.014998131348402709,
|
341 |
+
"alias": " - blimp_wh_vs_that_with_gap_long_distance"
|
342 |
+
}
|
343 |
+
},
|
344 |
+
"groups": {
|
345 |
+
"blimp": {
|
346 |
+
"acc,none": 0.8394328358208956,
|
347 |
+
"acc_stderr,none": 0.13653720128092459,
|
348 |
+
"alias": "blimp"
|
349 |
+
}
|
350 |
+
},
|
351 |
+
"configs": {
|
352 |
+
"blimp_adjunct_island": {
|
353 |
+
"task": "blimp_adjunct_island",
|
354 |
+
"group": "blimp",
|
355 |
+
"dataset_path": "blimp",
|
356 |
+
"dataset_name": "adjunct_island",
|
357 |
+
"validation_split": "train",
|
358 |
+
"doc_to_text": "",
|
359 |
+
"doc_to_target": 0,
|
360 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
361 |
+
"description": "",
|
362 |
+
"target_delimiter": " ",
|
363 |
+
"fewshot_delimiter": "\n\n",
|
364 |
+
"num_fewshot": 0,
|
365 |
+
"metric_list": [
|
366 |
+
{
|
367 |
+
"metric": "acc"
|
368 |
+
}
|
369 |
+
],
|
370 |
+
"output_type": "multiple_choice",
|
371 |
+
"repeats": 1,
|
372 |
+
"should_decontaminate": true,
|
373 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
374 |
+
"metadata": {
|
375 |
+
"version": 1.0
|
376 |
+
}
|
377 |
+
},
|
378 |
+
"blimp_anaphor_gender_agreement": {
|
379 |
+
"task": "blimp_anaphor_gender_agreement",
|
380 |
+
"group": "blimp",
|
381 |
+
"dataset_path": "blimp",
|
382 |
+
"dataset_name": "anaphor_gender_agreement",
|
383 |
+
"validation_split": "train",
|
384 |
+
"doc_to_text": "",
|
385 |
+
"doc_to_target": 0,
|
386 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
387 |
+
"description": "",
|
388 |
+
"target_delimiter": " ",
|
389 |
+
"fewshot_delimiter": "\n\n",
|
390 |
+
"num_fewshot": 0,
|
391 |
+
"metric_list": [
|
392 |
+
{
|
393 |
+
"metric": "acc"
|
394 |
+
}
|
395 |
+
],
|
396 |
+
"output_type": "multiple_choice",
|
397 |
+
"repeats": 1,
|
398 |
+
"should_decontaminate": true,
|
399 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
400 |
+
"metadata": {
|
401 |
+
"version": 1.0
|
402 |
+
}
|
403 |
+
},
|
404 |
+
"blimp_anaphor_number_agreement": {
|
405 |
+
"task": "blimp_anaphor_number_agreement",
|
406 |
+
"group": "blimp",
|
407 |
+
"dataset_path": "blimp",
|
408 |
+
"dataset_name": "anaphor_number_agreement",
|
409 |
+
"validation_split": "train",
|
410 |
+
"doc_to_text": "",
|
411 |
+
"doc_to_target": 0,
|
412 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
413 |
+
"description": "",
|
414 |
+
"target_delimiter": " ",
|
415 |
+
"fewshot_delimiter": "\n\n",
|
416 |
+
"num_fewshot": 0,
|
417 |
+
"metric_list": [
|
418 |
+
{
|
419 |
+
"metric": "acc"
|
420 |
+
}
|
421 |
+
],
|
422 |
+
"output_type": "multiple_choice",
|
423 |
+
"repeats": 1,
|
424 |
+
"should_decontaminate": true,
|
425 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
426 |
+
"metadata": {
|
427 |
+
"version": 1.0
|
428 |
+
}
|
429 |
+
},
|
430 |
+
"blimp_animate_subject_passive": {
|
431 |
+
"task": "blimp_animate_subject_passive",
|
432 |
+
"group": "blimp",
|
433 |
+
"dataset_path": "blimp",
|
434 |
+
"dataset_name": "animate_subject_passive",
|
435 |
+
"validation_split": "train",
|
436 |
+
"doc_to_text": "",
|
437 |
+
"doc_to_target": 0,
|
438 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
439 |
+
"description": "",
|
440 |
+
"target_delimiter": " ",
|
441 |
+
"fewshot_delimiter": "\n\n",
|
442 |
+
"num_fewshot": 0,
|
443 |
+
"metric_list": [
|
444 |
+
{
|
445 |
+
"metric": "acc"
|
446 |
+
}
|
447 |
+
],
|
448 |
+
"output_type": "multiple_choice",
|
449 |
+
"repeats": 1,
|
450 |
+
"should_decontaminate": true,
|
451 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
452 |
+
"metadata": {
|
453 |
+
"version": 1.0
|
454 |
+
}
|
455 |
+
},
|
456 |
+
"blimp_animate_subject_trans": {
|
457 |
+
"task": "blimp_animate_subject_trans",
|
458 |
+
"group": "blimp",
|
459 |
+
"dataset_path": "blimp",
|
460 |
+
"dataset_name": "animate_subject_trans",
|
461 |
+
"validation_split": "train",
|
462 |
+
"doc_to_text": "",
|
463 |
+
"doc_to_target": 0,
|
464 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
465 |
+
"description": "",
|
466 |
+
"target_delimiter": " ",
|
467 |
+
"fewshot_delimiter": "\n\n",
|
468 |
+
"num_fewshot": 0,
|
469 |
+
"metric_list": [
|
470 |
+
{
|
471 |
+
"metric": "acc"
|
472 |
+
}
|
473 |
+
],
|
474 |
+
"output_type": "multiple_choice",
|
475 |
+
"repeats": 1,
|
476 |
+
"should_decontaminate": true,
|
477 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
478 |
+
"metadata": {
|
479 |
+
"version": 1.0
|
480 |
+
}
|
481 |
+
},
|
482 |
+
"blimp_causative": {
|
483 |
+
"task": "blimp_causative",
|
484 |
+
"group": "blimp",
|
485 |
+
"dataset_path": "blimp",
|
486 |
+
"dataset_name": "causative",
|
487 |
+
"validation_split": "train",
|
488 |
+
"doc_to_text": "",
|
489 |
+
"doc_to_target": 0,
|
490 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
491 |
+
"description": "",
|
492 |
+
"target_delimiter": " ",
|
493 |
+
"fewshot_delimiter": "\n\n",
|
494 |
+
"num_fewshot": 0,
|
495 |
+
"metric_list": [
|
496 |
+
{
|
497 |
+
"metric": "acc"
|
498 |
+
}
|
499 |
+
],
|
500 |
+
"output_type": "multiple_choice",
|
501 |
+
"repeats": 1,
|
502 |
+
"should_decontaminate": true,
|
503 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
504 |
+
"metadata": {
|
505 |
+
"version": 1.0
|
506 |
+
}
|
507 |
+
},
|
508 |
+
"blimp_complex_NP_island": {
|
509 |
+
"task": "blimp_complex_NP_island",
|
510 |
+
"group": "blimp",
|
511 |
+
"dataset_path": "blimp",
|
512 |
+
"dataset_name": "complex_NP_island",
|
513 |
+
"validation_split": "train",
|
514 |
+
"doc_to_text": "",
|
515 |
+
"doc_to_target": 0,
|
516 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
517 |
+
"description": "",
|
518 |
+
"target_delimiter": " ",
|
519 |
+
"fewshot_delimiter": "\n\n",
|
520 |
+
"num_fewshot": 0,
|
521 |
+
"metric_list": [
|
522 |
+
{
|
523 |
+
"metric": "acc"
|
524 |
+
}
|
525 |
+
],
|
526 |
+
"output_type": "multiple_choice",
|
527 |
+
"repeats": 1,
|
528 |
+
"should_decontaminate": true,
|
529 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
530 |
+
"metadata": {
|
531 |
+
"version": 1.0
|
532 |
+
}
|
533 |
+
},
|
534 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
535 |
+
"task": "blimp_coordinate_structure_constraint_complex_left_branch",
|
536 |
+
"group": "blimp",
|
537 |
+
"dataset_path": "blimp",
|
538 |
+
"dataset_name": "coordinate_structure_constraint_complex_left_branch",
|
539 |
+
"validation_split": "train",
|
540 |
+
"doc_to_text": "",
|
541 |
+
"doc_to_target": 0,
|
542 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
543 |
+
"description": "",
|
544 |
+
"target_delimiter": " ",
|
545 |
+
"fewshot_delimiter": "\n\n",
|
546 |
+
"num_fewshot": 0,
|
547 |
+
"metric_list": [
|
548 |
+
{
|
549 |
+
"metric": "acc"
|
550 |
+
}
|
551 |
+
],
|
552 |
+
"output_type": "multiple_choice",
|
553 |
+
"repeats": 1,
|
554 |
+
"should_decontaminate": true,
|
555 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
556 |
+
"metadata": {
|
557 |
+
"version": 1.0
|
558 |
+
}
|
559 |
+
},
|
560 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
561 |
+
"task": "blimp_coordinate_structure_constraint_object_extraction",
|
562 |
+
"group": "blimp",
|
563 |
+
"dataset_path": "blimp",
|
564 |
+
"dataset_name": "coordinate_structure_constraint_object_extraction",
|
565 |
+
"validation_split": "train",
|
566 |
+
"doc_to_text": "",
|
567 |
+
"doc_to_target": 0,
|
568 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
569 |
+
"description": "",
|
570 |
+
"target_delimiter": " ",
|
571 |
+
"fewshot_delimiter": "\n\n",
|
572 |
+
"num_fewshot": 0,
|
573 |
+
"metric_list": [
|
574 |
+
{
|
575 |
+
"metric": "acc"
|
576 |
+
}
|
577 |
+
],
|
578 |
+
"output_type": "multiple_choice",
|
579 |
+
"repeats": 1,
|
580 |
+
"should_decontaminate": true,
|
581 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
582 |
+
"metadata": {
|
583 |
+
"version": 1.0
|
584 |
+
}
|
585 |
+
},
|
586 |
+
"blimp_determiner_noun_agreement_1": {
|
587 |
+
"task": "blimp_determiner_noun_agreement_1",
|
588 |
+
"group": "blimp",
|
589 |
+
"dataset_path": "blimp",
|
590 |
+
"dataset_name": "determiner_noun_agreement_1",
|
591 |
+
"validation_split": "train",
|
592 |
+
"doc_to_text": "",
|
593 |
+
"doc_to_target": 0,
|
594 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
595 |
+
"description": "",
|
596 |
+
"target_delimiter": " ",
|
597 |
+
"fewshot_delimiter": "\n\n",
|
598 |
+
"num_fewshot": 0,
|
599 |
+
"metric_list": [
|
600 |
+
{
|
601 |
+
"metric": "acc"
|
602 |
+
}
|
603 |
+
],
|
604 |
+
"output_type": "multiple_choice",
|
605 |
+
"repeats": 1,
|
606 |
+
"should_decontaminate": true,
|
607 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
608 |
+
"metadata": {
|
609 |
+
"version": 1.0
|
610 |
+
}
|
611 |
+
},
|
612 |
+
"blimp_determiner_noun_agreement_2": {
|
613 |
+
"task": "blimp_determiner_noun_agreement_2",
|
614 |
+
"group": "blimp",
|
615 |
+
"dataset_path": "blimp",
|
616 |
+
"dataset_name": "determiner_noun_agreement_2",
|
617 |
+
"validation_split": "train",
|
618 |
+
"doc_to_text": "",
|
619 |
+
"doc_to_target": 0,
|
620 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
621 |
+
"description": "",
|
622 |
+
"target_delimiter": " ",
|
623 |
+
"fewshot_delimiter": "\n\n",
|
624 |
+
"num_fewshot": 0,
|
625 |
+
"metric_list": [
|
626 |
+
{
|
627 |
+
"metric": "acc"
|
628 |
+
}
|
629 |
+
],
|
630 |
+
"output_type": "multiple_choice",
|
631 |
+
"repeats": 1,
|
632 |
+
"should_decontaminate": true,
|
633 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
634 |
+
"metadata": {
|
635 |
+
"version": 1.0
|
636 |
+
}
|
637 |
+
},
|
638 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
639 |
+
"task": "blimp_determiner_noun_agreement_irregular_1",
|
640 |
+
"group": "blimp",
|
641 |
+
"dataset_path": "blimp",
|
642 |
+
"dataset_name": "determiner_noun_agreement_irregular_1",
|
643 |
+
"validation_split": "train",
|
644 |
+
"doc_to_text": "",
|
645 |
+
"doc_to_target": 0,
|
646 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
647 |
+
"description": "",
|
648 |
+
"target_delimiter": " ",
|
649 |
+
"fewshot_delimiter": "\n\n",
|
650 |
+
"num_fewshot": 0,
|
651 |
+
"metric_list": [
|
652 |
+
{
|
653 |
+
"metric": "acc"
|
654 |
+
}
|
655 |
+
],
|
656 |
+
"output_type": "multiple_choice",
|
657 |
+
"repeats": 1,
|
658 |
+
"should_decontaminate": true,
|
659 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
660 |
+
"metadata": {
|
661 |
+
"version": 1.0
|
662 |
+
}
|
663 |
+
},
|
664 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
665 |
+
"task": "blimp_determiner_noun_agreement_irregular_2",
|
666 |
+
"group": "blimp",
|
667 |
+
"dataset_path": "blimp",
|
668 |
+
"dataset_name": "determiner_noun_agreement_irregular_2",
|
669 |
+
"validation_split": "train",
|
670 |
+
"doc_to_text": "",
|
671 |
+
"doc_to_target": 0,
|
672 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
673 |
+
"description": "",
|
674 |
+
"target_delimiter": " ",
|
675 |
+
"fewshot_delimiter": "\n\n",
|
676 |
+
"num_fewshot": 0,
|
677 |
+
"metric_list": [
|
678 |
+
{
|
679 |
+
"metric": "acc"
|
680 |
+
}
|
681 |
+
],
|
682 |
+
"output_type": "multiple_choice",
|
683 |
+
"repeats": 1,
|
684 |
+
"should_decontaminate": true,
|
685 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
686 |
+
"metadata": {
|
687 |
+
"version": 1.0
|
688 |
+
}
|
689 |
+
},
|
690 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
691 |
+
"task": "blimp_determiner_noun_agreement_with_adj_2",
|
692 |
+
"group": "blimp",
|
693 |
+
"dataset_path": "blimp",
|
694 |
+
"dataset_name": "determiner_noun_agreement_with_adj_2",
|
695 |
+
"validation_split": "train",
|
696 |
+
"doc_to_text": "",
|
697 |
+
"doc_to_target": 0,
|
698 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
699 |
+
"description": "",
|
700 |
+
"target_delimiter": " ",
|
701 |
+
"fewshot_delimiter": "\n\n",
|
702 |
+
"num_fewshot": 0,
|
703 |
+
"metric_list": [
|
704 |
+
{
|
705 |
+
"metric": "acc"
|
706 |
+
}
|
707 |
+
],
|
708 |
+
"output_type": "multiple_choice",
|
709 |
+
"repeats": 1,
|
710 |
+
"should_decontaminate": true,
|
711 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
712 |
+
"metadata": {
|
713 |
+
"version": 1.0
|
714 |
+
}
|
715 |
+
},
|
716 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
717 |
+
"task": "blimp_determiner_noun_agreement_with_adj_irregular_1",
|
718 |
+
"group": "blimp",
|
719 |
+
"dataset_path": "blimp",
|
720 |
+
"dataset_name": "determiner_noun_agreement_with_adj_irregular_1",
|
721 |
+
"validation_split": "train",
|
722 |
+
"doc_to_text": "",
|
723 |
+
"doc_to_target": 0,
|
724 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
725 |
+
"description": "",
|
726 |
+
"target_delimiter": " ",
|
727 |
+
"fewshot_delimiter": "\n\n",
|
728 |
+
"num_fewshot": 0,
|
729 |
+
"metric_list": [
|
730 |
+
{
|
731 |
+
"metric": "acc"
|
732 |
+
}
|
733 |
+
],
|
734 |
+
"output_type": "multiple_choice",
|
735 |
+
"repeats": 1,
|
736 |
+
"should_decontaminate": true,
|
737 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
738 |
+
"metadata": {
|
739 |
+
"version": 1.0
|
740 |
+
}
|
741 |
+
},
|
742 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
743 |
+
"task": "blimp_determiner_noun_agreement_with_adj_irregular_2",
|
744 |
+
"group": "blimp",
|
745 |
+
"dataset_path": "blimp",
|
746 |
+
"dataset_name": "determiner_noun_agreement_with_adj_irregular_2",
|
747 |
+
"validation_split": "train",
|
748 |
+
"doc_to_text": "",
|
749 |
+
"doc_to_target": 0,
|
750 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
751 |
+
"description": "",
|
752 |
+
"target_delimiter": " ",
|
753 |
+
"fewshot_delimiter": "\n\n",
|
754 |
+
"num_fewshot": 0,
|
755 |
+
"metric_list": [
|
756 |
+
{
|
757 |
+
"metric": "acc"
|
758 |
+
}
|
759 |
+
],
|
760 |
+
"output_type": "multiple_choice",
|
761 |
+
"repeats": 1,
|
762 |
+
"should_decontaminate": true,
|
763 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
764 |
+
"metadata": {
|
765 |
+
"version": 1.0
|
766 |
+
}
|
767 |
+
},
|
768 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
769 |
+
"task": "blimp_determiner_noun_agreement_with_adjective_1",
|
770 |
+
"group": "blimp",
|
771 |
+
"dataset_path": "blimp",
|
772 |
+
"dataset_name": "determiner_noun_agreement_with_adjective_1",
|
773 |
+
"validation_split": "train",
|
774 |
+
"doc_to_text": "",
|
775 |
+
"doc_to_target": 0,
|
776 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
777 |
+
"description": "",
|
778 |
+
"target_delimiter": " ",
|
779 |
+
"fewshot_delimiter": "\n\n",
|
780 |
+
"num_fewshot": 0,
|
781 |
+
"metric_list": [
|
782 |
+
{
|
783 |
+
"metric": "acc"
|
784 |
+
}
|
785 |
+
],
|
786 |
+
"output_type": "multiple_choice",
|
787 |
+
"repeats": 1,
|
788 |
+
"should_decontaminate": true,
|
789 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
790 |
+
"metadata": {
|
791 |
+
"version": 1.0
|
792 |
+
}
|
793 |
+
},
|
794 |
+
"blimp_distractor_agreement_relational_noun": {
|
795 |
+
"task": "blimp_distractor_agreement_relational_noun",
|
796 |
+
"group": "blimp",
|
797 |
+
"dataset_path": "blimp",
|
798 |
+
"dataset_name": "distractor_agreement_relational_noun",
|
799 |
+
"validation_split": "train",
|
800 |
+
"doc_to_text": "",
|
801 |
+
"doc_to_target": 0,
|
802 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
803 |
+
"description": "",
|
804 |
+
"target_delimiter": " ",
|
805 |
+
"fewshot_delimiter": "\n\n",
|
806 |
+
"num_fewshot": 0,
|
807 |
+
"metric_list": [
|
808 |
+
{
|
809 |
+
"metric": "acc"
|
810 |
+
}
|
811 |
+
],
|
812 |
+
"output_type": "multiple_choice",
|
813 |
+
"repeats": 1,
|
814 |
+
"should_decontaminate": true,
|
815 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
816 |
+
"metadata": {
|
817 |
+
"version": 1.0
|
818 |
+
}
|
819 |
+
},
|
820 |
+
"blimp_distractor_agreement_relative_clause": {
|
821 |
+
"task": "blimp_distractor_agreement_relative_clause",
|
822 |
+
"group": "blimp",
|
823 |
+
"dataset_path": "blimp",
|
824 |
+
"dataset_name": "distractor_agreement_relative_clause",
|
825 |
+
"validation_split": "train",
|
826 |
+
"doc_to_text": "",
|
827 |
+
"doc_to_target": 0,
|
828 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
829 |
+
"description": "",
|
830 |
+
"target_delimiter": " ",
|
831 |
+
"fewshot_delimiter": "\n\n",
|
832 |
+
"num_fewshot": 0,
|
833 |
+
"metric_list": [
|
834 |
+
{
|
835 |
+
"metric": "acc"
|
836 |
+
}
|
837 |
+
],
|
838 |
+
"output_type": "multiple_choice",
|
839 |
+
"repeats": 1,
|
840 |
+
"should_decontaminate": true,
|
841 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
842 |
+
"metadata": {
|
843 |
+
"version": 1.0
|
844 |
+
}
|
845 |
+
},
|
846 |
+
"blimp_drop_argument": {
|
847 |
+
"task": "blimp_drop_argument",
|
848 |
+
"group": "blimp",
|
849 |
+
"dataset_path": "blimp",
|
850 |
+
"dataset_name": "drop_argument",
|
851 |
+
"validation_split": "train",
|
852 |
+
"doc_to_text": "",
|
853 |
+
"doc_to_target": 0,
|
854 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
855 |
+
"description": "",
|
856 |
+
"target_delimiter": " ",
|
857 |
+
"fewshot_delimiter": "\n\n",
|
858 |
+
"num_fewshot": 0,
|
859 |
+
"metric_list": [
|
860 |
+
{
|
861 |
+
"metric": "acc"
|
862 |
+
}
|
863 |
+
],
|
864 |
+
"output_type": "multiple_choice",
|
865 |
+
"repeats": 1,
|
866 |
+
"should_decontaminate": true,
|
867 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
868 |
+
"metadata": {
|
869 |
+
"version": 1.0
|
870 |
+
}
|
871 |
+
},
|
872 |
+
"blimp_ellipsis_n_bar_1": {
|
873 |
+
"task": "blimp_ellipsis_n_bar_1",
|
874 |
+
"group": "blimp",
|
875 |
+
"dataset_path": "blimp",
|
876 |
+
"dataset_name": "ellipsis_n_bar_1",
|
877 |
+
"validation_split": "train",
|
878 |
+
"doc_to_text": "",
|
879 |
+
"doc_to_target": 0,
|
880 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
881 |
+
"description": "",
|
882 |
+
"target_delimiter": " ",
|
883 |
+
"fewshot_delimiter": "\n\n",
|
884 |
+
"num_fewshot": 0,
|
885 |
+
"metric_list": [
|
886 |
+
{
|
887 |
+
"metric": "acc"
|
888 |
+
}
|
889 |
+
],
|
890 |
+
"output_type": "multiple_choice",
|
891 |
+
"repeats": 1,
|
892 |
+
"should_decontaminate": true,
|
893 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
894 |
+
"metadata": {
|
895 |
+
"version": 1.0
|
896 |
+
}
|
897 |
+
},
|
898 |
+
"blimp_ellipsis_n_bar_2": {
|
899 |
+
"task": "blimp_ellipsis_n_bar_2",
|
900 |
+
"group": "blimp",
|
901 |
+
"dataset_path": "blimp",
|
902 |
+
"dataset_name": "ellipsis_n_bar_2",
|
903 |
+
"validation_split": "train",
|
904 |
+
"doc_to_text": "",
|
905 |
+
"doc_to_target": 0,
|
906 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
907 |
+
"description": "",
|
908 |
+
"target_delimiter": " ",
|
909 |
+
"fewshot_delimiter": "\n\n",
|
910 |
+
"num_fewshot": 0,
|
911 |
+
"metric_list": [
|
912 |
+
{
|
913 |
+
"metric": "acc"
|
914 |
+
}
|
915 |
+
],
|
916 |
+
"output_type": "multiple_choice",
|
917 |
+
"repeats": 1,
|
918 |
+
"should_decontaminate": true,
|
919 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
920 |
+
"metadata": {
|
921 |
+
"version": 1.0
|
922 |
+
}
|
923 |
+
},
|
924 |
+
"blimp_existential_there_object_raising": {
|
925 |
+
"task": "blimp_existential_there_object_raising",
|
926 |
+
"group": "blimp",
|
927 |
+
"dataset_path": "blimp",
|
928 |
+
"dataset_name": "existential_there_object_raising",
|
929 |
+
"validation_split": "train",
|
930 |
+
"doc_to_text": "",
|
931 |
+
"doc_to_target": 0,
|
932 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
933 |
+
"description": "",
|
934 |
+
"target_delimiter": " ",
|
935 |
+
"fewshot_delimiter": "\n\n",
|
936 |
+
"num_fewshot": 0,
|
937 |
+
"metric_list": [
|
938 |
+
{
|
939 |
+
"metric": "acc"
|
940 |
+
}
|
941 |
+
],
|
942 |
+
"output_type": "multiple_choice",
|
943 |
+
"repeats": 1,
|
944 |
+
"should_decontaminate": true,
|
945 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
946 |
+
"metadata": {
|
947 |
+
"version": 1.0
|
948 |
+
}
|
949 |
+
},
|
950 |
+
"blimp_existential_there_quantifiers_1": {
|
951 |
+
"task": "blimp_existential_there_quantifiers_1",
|
952 |
+
"group": "blimp",
|
953 |
+
"dataset_path": "blimp",
|
954 |
+
"dataset_name": "existential_there_quantifiers_1",
|
955 |
+
"validation_split": "train",
|
956 |
+
"doc_to_text": "",
|
957 |
+
"doc_to_target": 0,
|
958 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
959 |
+
"description": "",
|
960 |
+
"target_delimiter": " ",
|
961 |
+
"fewshot_delimiter": "\n\n",
|
962 |
+
"num_fewshot": 0,
|
963 |
+
"metric_list": [
|
964 |
+
{
|
965 |
+
"metric": "acc"
|
966 |
+
}
|
967 |
+
],
|
968 |
+
"output_type": "multiple_choice",
|
969 |
+
"repeats": 1,
|
970 |
+
"should_decontaminate": true,
|
971 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
972 |
+
"metadata": {
|
973 |
+
"version": 1.0
|
974 |
+
}
|
975 |
+
},
|
976 |
+
"blimp_existential_there_quantifiers_2": {
|
977 |
+
"task": "blimp_existential_there_quantifiers_2",
|
978 |
+
"group": "blimp",
|
979 |
+
"dataset_path": "blimp",
|
980 |
+
"dataset_name": "existential_there_quantifiers_2",
|
981 |
+
"validation_split": "train",
|
982 |
+
"doc_to_text": "",
|
983 |
+
"doc_to_target": 0,
|
984 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
985 |
+
"description": "",
|
986 |
+
"target_delimiter": " ",
|
987 |
+
"fewshot_delimiter": "\n\n",
|
988 |
+
"num_fewshot": 0,
|
989 |
+
"metric_list": [
|
990 |
+
{
|
991 |
+
"metric": "acc"
|
992 |
+
}
|
993 |
+
],
|
994 |
+
"output_type": "multiple_choice",
|
995 |
+
"repeats": 1,
|
996 |
+
"should_decontaminate": true,
|
997 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
998 |
+
"metadata": {
|
999 |
+
"version": 1.0
|
1000 |
+
}
|
1001 |
+
},
|
1002 |
+
"blimp_existential_there_subject_raising": {
|
1003 |
+
"task": "blimp_existential_there_subject_raising",
|
1004 |
+
"group": "blimp",
|
1005 |
+
"dataset_path": "blimp",
|
1006 |
+
"dataset_name": "existential_there_subject_raising",
|
1007 |
+
"validation_split": "train",
|
1008 |
+
"doc_to_text": "",
|
1009 |
+
"doc_to_target": 0,
|
1010 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1011 |
+
"description": "",
|
1012 |
+
"target_delimiter": " ",
|
1013 |
+
"fewshot_delimiter": "\n\n",
|
1014 |
+
"num_fewshot": 0,
|
1015 |
+
"metric_list": [
|
1016 |
+
{
|
1017 |
+
"metric": "acc"
|
1018 |
+
}
|
1019 |
+
],
|
1020 |
+
"output_type": "multiple_choice",
|
1021 |
+
"repeats": 1,
|
1022 |
+
"should_decontaminate": true,
|
1023 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1024 |
+
"metadata": {
|
1025 |
+
"version": 1.0
|
1026 |
+
}
|
1027 |
+
},
|
1028 |
+
"blimp_expletive_it_object_raising": {
|
1029 |
+
"task": "blimp_expletive_it_object_raising",
|
1030 |
+
"group": "blimp",
|
1031 |
+
"dataset_path": "blimp",
|
1032 |
+
"dataset_name": "expletive_it_object_raising",
|
1033 |
+
"validation_split": "train",
|
1034 |
+
"doc_to_text": "",
|
1035 |
+
"doc_to_target": 0,
|
1036 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1037 |
+
"description": "",
|
1038 |
+
"target_delimiter": " ",
|
1039 |
+
"fewshot_delimiter": "\n\n",
|
1040 |
+
"num_fewshot": 0,
|
1041 |
+
"metric_list": [
|
1042 |
+
{
|
1043 |
+
"metric": "acc"
|
1044 |
+
}
|
1045 |
+
],
|
1046 |
+
"output_type": "multiple_choice",
|
1047 |
+
"repeats": 1,
|
1048 |
+
"should_decontaminate": true,
|
1049 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1050 |
+
"metadata": {
|
1051 |
+
"version": 1.0
|
1052 |
+
}
|
1053 |
+
},
|
1054 |
+
"blimp_inchoative": {
|
1055 |
+
"task": "blimp_inchoative",
|
1056 |
+
"group": "blimp",
|
1057 |
+
"dataset_path": "blimp",
|
1058 |
+
"dataset_name": "inchoative",
|
1059 |
+
"validation_split": "train",
|
1060 |
+
"doc_to_text": "",
|
1061 |
+
"doc_to_target": 0,
|
1062 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1063 |
+
"description": "",
|
1064 |
+
"target_delimiter": " ",
|
1065 |
+
"fewshot_delimiter": "\n\n",
|
1066 |
+
"num_fewshot": 0,
|
1067 |
+
"metric_list": [
|
1068 |
+
{
|
1069 |
+
"metric": "acc"
|
1070 |
+
}
|
1071 |
+
],
|
1072 |
+
"output_type": "multiple_choice",
|
1073 |
+
"repeats": 1,
|
1074 |
+
"should_decontaminate": true,
|
1075 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1076 |
+
"metadata": {
|
1077 |
+
"version": 1.0
|
1078 |
+
}
|
1079 |
+
},
|
1080 |
+
"blimp_intransitive": {
|
1081 |
+
"task": "blimp_intransitive",
|
1082 |
+
"group": "blimp",
|
1083 |
+
"dataset_path": "blimp",
|
1084 |
+
"dataset_name": "intransitive",
|
1085 |
+
"validation_split": "train",
|
1086 |
+
"doc_to_text": "",
|
1087 |
+
"doc_to_target": 0,
|
1088 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1089 |
+
"description": "",
|
1090 |
+
"target_delimiter": " ",
|
1091 |
+
"fewshot_delimiter": "\n\n",
|
1092 |
+
"num_fewshot": 0,
|
1093 |
+
"metric_list": [
|
1094 |
+
{
|
1095 |
+
"metric": "acc"
|
1096 |
+
}
|
1097 |
+
],
|
1098 |
+
"output_type": "multiple_choice",
|
1099 |
+
"repeats": 1,
|
1100 |
+
"should_decontaminate": true,
|
1101 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1102 |
+
"metadata": {
|
1103 |
+
"version": 1.0
|
1104 |
+
}
|
1105 |
+
},
|
1106 |
+
"blimp_irregular_past_participle_adjectives": {
|
1107 |
+
"task": "blimp_irregular_past_participle_adjectives",
|
1108 |
+
"group": "blimp",
|
1109 |
+
"dataset_path": "blimp",
|
1110 |
+
"dataset_name": "irregular_past_participle_adjectives",
|
1111 |
+
"validation_split": "train",
|
1112 |
+
"doc_to_text": "",
|
1113 |
+
"doc_to_target": 0,
|
1114 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1115 |
+
"description": "",
|
1116 |
+
"target_delimiter": " ",
|
1117 |
+
"fewshot_delimiter": "\n\n",
|
1118 |
+
"num_fewshot": 0,
|
1119 |
+
"metric_list": [
|
1120 |
+
{
|
1121 |
+
"metric": "acc"
|
1122 |
+
}
|
1123 |
+
],
|
1124 |
+
"output_type": "multiple_choice",
|
1125 |
+
"repeats": 1,
|
1126 |
+
"should_decontaminate": true,
|
1127 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1128 |
+
"metadata": {
|
1129 |
+
"version": 1.0
|
1130 |
+
}
|
1131 |
+
},
|
1132 |
+
"blimp_irregular_past_participle_verbs": {
|
1133 |
+
"task": "blimp_irregular_past_participle_verbs",
|
1134 |
+
"group": "blimp",
|
1135 |
+
"dataset_path": "blimp",
|
1136 |
+
"dataset_name": "irregular_past_participle_verbs",
|
1137 |
+
"validation_split": "train",
|
1138 |
+
"doc_to_text": "",
|
1139 |
+
"doc_to_target": 0,
|
1140 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1141 |
+
"description": "",
|
1142 |
+
"target_delimiter": " ",
|
1143 |
+
"fewshot_delimiter": "\n\n",
|
1144 |
+
"num_fewshot": 0,
|
1145 |
+
"metric_list": [
|
1146 |
+
{
|
1147 |
+
"metric": "acc"
|
1148 |
+
}
|
1149 |
+
],
|
1150 |
+
"output_type": "multiple_choice",
|
1151 |
+
"repeats": 1,
|
1152 |
+
"should_decontaminate": true,
|
1153 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1154 |
+
"metadata": {
|
1155 |
+
"version": 1.0
|
1156 |
+
}
|
1157 |
+
},
|
1158 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
1159 |
+
"task": "blimp_irregular_plural_subject_verb_agreement_1",
|
1160 |
+
"group": "blimp",
|
1161 |
+
"dataset_path": "blimp",
|
1162 |
+
"dataset_name": "irregular_plural_subject_verb_agreement_1",
|
1163 |
+
"validation_split": "train",
|
1164 |
+
"doc_to_text": "",
|
1165 |
+
"doc_to_target": 0,
|
1166 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1167 |
+
"description": "",
|
1168 |
+
"target_delimiter": " ",
|
1169 |
+
"fewshot_delimiter": "\n\n",
|
1170 |
+
"num_fewshot": 0,
|
1171 |
+
"metric_list": [
|
1172 |
+
{
|
1173 |
+
"metric": "acc"
|
1174 |
+
}
|
1175 |
+
],
|
1176 |
+
"output_type": "multiple_choice",
|
1177 |
+
"repeats": 1,
|
1178 |
+
"should_decontaminate": true,
|
1179 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1180 |
+
"metadata": {
|
1181 |
+
"version": 1.0
|
1182 |
+
}
|
1183 |
+
},
|
1184 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
1185 |
+
"task": "blimp_irregular_plural_subject_verb_agreement_2",
|
1186 |
+
"group": "blimp",
|
1187 |
+
"dataset_path": "blimp",
|
1188 |
+
"dataset_name": "irregular_plural_subject_verb_agreement_2",
|
1189 |
+
"validation_split": "train",
|
1190 |
+
"doc_to_text": "",
|
1191 |
+
"doc_to_target": 0,
|
1192 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1193 |
+
"description": "",
|
1194 |
+
"target_delimiter": " ",
|
1195 |
+
"fewshot_delimiter": "\n\n",
|
1196 |
+
"num_fewshot": 0,
|
1197 |
+
"metric_list": [
|
1198 |
+
{
|
1199 |
+
"metric": "acc"
|
1200 |
+
}
|
1201 |
+
],
|
1202 |
+
"output_type": "multiple_choice",
|
1203 |
+
"repeats": 1,
|
1204 |
+
"should_decontaminate": true,
|
1205 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1206 |
+
"metadata": {
|
1207 |
+
"version": 1.0
|
1208 |
+
}
|
1209 |
+
},
|
1210 |
+
"blimp_left_branch_island_echo_question": {
|
1211 |
+
"task": "blimp_left_branch_island_echo_question",
|
1212 |
+
"group": "blimp",
|
1213 |
+
"dataset_path": "blimp",
|
1214 |
+
"dataset_name": "left_branch_island_echo_question",
|
1215 |
+
"validation_split": "train",
|
1216 |
+
"doc_to_text": "",
|
1217 |
+
"doc_to_target": 0,
|
1218 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1219 |
+
"description": "",
|
1220 |
+
"target_delimiter": " ",
|
1221 |
+
"fewshot_delimiter": "\n\n",
|
1222 |
+
"num_fewshot": 0,
|
1223 |
+
"metric_list": [
|
1224 |
+
{
|
1225 |
+
"metric": "acc"
|
1226 |
+
}
|
1227 |
+
],
|
1228 |
+
"output_type": "multiple_choice",
|
1229 |
+
"repeats": 1,
|
1230 |
+
"should_decontaminate": true,
|
1231 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1232 |
+
"metadata": {
|
1233 |
+
"version": 1.0
|
1234 |
+
}
|
1235 |
+
},
|
1236 |
+
"blimp_left_branch_island_simple_question": {
|
1237 |
+
"task": "blimp_left_branch_island_simple_question",
|
1238 |
+
"group": "blimp",
|
1239 |
+
"dataset_path": "blimp",
|
1240 |
+
"dataset_name": "left_branch_island_simple_question",
|
1241 |
+
"validation_split": "train",
|
1242 |
+
"doc_to_text": "",
|
1243 |
+
"doc_to_target": 0,
|
1244 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1245 |
+
"description": "",
|
1246 |
+
"target_delimiter": " ",
|
1247 |
+
"fewshot_delimiter": "\n\n",
|
1248 |
+
"num_fewshot": 0,
|
1249 |
+
"metric_list": [
|
1250 |
+
{
|
1251 |
+
"metric": "acc"
|
1252 |
+
}
|
1253 |
+
],
|
1254 |
+
"output_type": "multiple_choice",
|
1255 |
+
"repeats": 1,
|
1256 |
+
"should_decontaminate": true,
|
1257 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1258 |
+
"metadata": {
|
1259 |
+
"version": 1.0
|
1260 |
+
}
|
1261 |
+
},
|
1262 |
+
"blimp_matrix_question_npi_licensor_present": {
|
1263 |
+
"task": "blimp_matrix_question_npi_licensor_present",
|
1264 |
+
"group": "blimp",
|
1265 |
+
"dataset_path": "blimp",
|
1266 |
+
"dataset_name": "matrix_question_npi_licensor_present",
|
1267 |
+
"validation_split": "train",
|
1268 |
+
"doc_to_text": "",
|
1269 |
+
"doc_to_target": 0,
|
1270 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1271 |
+
"description": "",
|
1272 |
+
"target_delimiter": " ",
|
1273 |
+
"fewshot_delimiter": "\n\n",
|
1274 |
+
"num_fewshot": 0,
|
1275 |
+
"metric_list": [
|
1276 |
+
{
|
1277 |
+
"metric": "acc"
|
1278 |
+
}
|
1279 |
+
],
|
1280 |
+
"output_type": "multiple_choice",
|
1281 |
+
"repeats": 1,
|
1282 |
+
"should_decontaminate": true,
|
1283 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1284 |
+
"metadata": {
|
1285 |
+
"version": 1.0
|
1286 |
+
}
|
1287 |
+
},
|
1288 |
+
"blimp_npi_present_1": {
|
1289 |
+
"task": "blimp_npi_present_1",
|
1290 |
+
"group": "blimp",
|
1291 |
+
"dataset_path": "blimp",
|
1292 |
+
"dataset_name": "npi_present_1",
|
1293 |
+
"validation_split": "train",
|
1294 |
+
"doc_to_text": "",
|
1295 |
+
"doc_to_target": 0,
|
1296 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1297 |
+
"description": "",
|
1298 |
+
"target_delimiter": " ",
|
1299 |
+
"fewshot_delimiter": "\n\n",
|
1300 |
+
"num_fewshot": 0,
|
1301 |
+
"metric_list": [
|
1302 |
+
{
|
1303 |
+
"metric": "acc"
|
1304 |
+
}
|
1305 |
+
],
|
1306 |
+
"output_type": "multiple_choice",
|
1307 |
+
"repeats": 1,
|
1308 |
+
"should_decontaminate": true,
|
1309 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1310 |
+
"metadata": {
|
1311 |
+
"version": 1.0
|
1312 |
+
}
|
1313 |
+
},
|
1314 |
+
"blimp_npi_present_2": {
|
1315 |
+
"task": "blimp_npi_present_2",
|
1316 |
+
"group": "blimp",
|
1317 |
+
"dataset_path": "blimp",
|
1318 |
+
"dataset_name": "npi_present_2",
|
1319 |
+
"validation_split": "train",
|
1320 |
+
"doc_to_text": "",
|
1321 |
+
"doc_to_target": 0,
|
1322 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1323 |
+
"description": "",
|
1324 |
+
"target_delimiter": " ",
|
1325 |
+
"fewshot_delimiter": "\n\n",
|
1326 |
+
"num_fewshot": 0,
|
1327 |
+
"metric_list": [
|
1328 |
+
{
|
1329 |
+
"metric": "acc"
|
1330 |
+
}
|
1331 |
+
],
|
1332 |
+
"output_type": "multiple_choice",
|
1333 |
+
"repeats": 1,
|
1334 |
+
"should_decontaminate": true,
|
1335 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1336 |
+
"metadata": {
|
1337 |
+
"version": 1.0
|
1338 |
+
}
|
1339 |
+
},
|
1340 |
+
"blimp_only_npi_licensor_present": {
|
1341 |
+
"task": "blimp_only_npi_licensor_present",
|
1342 |
+
"group": "blimp",
|
1343 |
+
"dataset_path": "blimp",
|
1344 |
+
"dataset_name": "only_npi_licensor_present",
|
1345 |
+
"validation_split": "train",
|
1346 |
+
"doc_to_text": "",
|
1347 |
+
"doc_to_target": 0,
|
1348 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1349 |
+
"description": "",
|
1350 |
+
"target_delimiter": " ",
|
1351 |
+
"fewshot_delimiter": "\n\n",
|
1352 |
+
"num_fewshot": 0,
|
1353 |
+
"metric_list": [
|
1354 |
+
{
|
1355 |
+
"metric": "acc"
|
1356 |
+
}
|
1357 |
+
],
|
1358 |
+
"output_type": "multiple_choice",
|
1359 |
+
"repeats": 1,
|
1360 |
+
"should_decontaminate": true,
|
1361 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1362 |
+
"metadata": {
|
1363 |
+
"version": 1.0
|
1364 |
+
}
|
1365 |
+
},
|
1366 |
+
"blimp_only_npi_scope": {
|
1367 |
+
"task": "blimp_only_npi_scope",
|
1368 |
+
"group": "blimp",
|
1369 |
+
"dataset_path": "blimp",
|
1370 |
+
"dataset_name": "only_npi_scope",
|
1371 |
+
"validation_split": "train",
|
1372 |
+
"doc_to_text": "",
|
1373 |
+
"doc_to_target": 0,
|
1374 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1375 |
+
"description": "",
|
1376 |
+
"target_delimiter": " ",
|
1377 |
+
"fewshot_delimiter": "\n\n",
|
1378 |
+
"num_fewshot": 0,
|
1379 |
+
"metric_list": [
|
1380 |
+
{
|
1381 |
+
"metric": "acc"
|
1382 |
+
}
|
1383 |
+
],
|
1384 |
+
"output_type": "multiple_choice",
|
1385 |
+
"repeats": 1,
|
1386 |
+
"should_decontaminate": true,
|
1387 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1388 |
+
"metadata": {
|
1389 |
+
"version": 1.0
|
1390 |
+
}
|
1391 |
+
},
|
1392 |
+
"blimp_passive_1": {
|
1393 |
+
"task": "blimp_passive_1",
|
1394 |
+
"group": "blimp",
|
1395 |
+
"dataset_path": "blimp",
|
1396 |
+
"dataset_name": "passive_1",
|
1397 |
+
"validation_split": "train",
|
1398 |
+
"doc_to_text": "",
|
1399 |
+
"doc_to_target": 0,
|
1400 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1401 |
+
"description": "",
|
1402 |
+
"target_delimiter": " ",
|
1403 |
+
"fewshot_delimiter": "\n\n",
|
1404 |
+
"num_fewshot": 0,
|
1405 |
+
"metric_list": [
|
1406 |
+
{
|
1407 |
+
"metric": "acc"
|
1408 |
+
}
|
1409 |
+
],
|
1410 |
+
"output_type": "multiple_choice",
|
1411 |
+
"repeats": 1,
|
1412 |
+
"should_decontaminate": true,
|
1413 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1414 |
+
"metadata": {
|
1415 |
+
"version": 1.0
|
1416 |
+
}
|
1417 |
+
},
|
1418 |
+
"blimp_passive_2": {
|
1419 |
+
"task": "blimp_passive_2",
|
1420 |
+
"group": "blimp",
|
1421 |
+
"dataset_path": "blimp",
|
1422 |
+
"dataset_name": "passive_2",
|
1423 |
+
"validation_split": "train",
|
1424 |
+
"doc_to_text": "",
|
1425 |
+
"doc_to_target": 0,
|
1426 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1427 |
+
"description": "",
|
1428 |
+
"target_delimiter": " ",
|
1429 |
+
"fewshot_delimiter": "\n\n",
|
1430 |
+
"num_fewshot": 0,
|
1431 |
+
"metric_list": [
|
1432 |
+
{
|
1433 |
+
"metric": "acc"
|
1434 |
+
}
|
1435 |
+
],
|
1436 |
+
"output_type": "multiple_choice",
|
1437 |
+
"repeats": 1,
|
1438 |
+
"should_decontaminate": true,
|
1439 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1440 |
+
"metadata": {
|
1441 |
+
"version": 1.0
|
1442 |
+
}
|
1443 |
+
},
|
1444 |
+
"blimp_principle_A_c_command": {
|
1445 |
+
"task": "blimp_principle_A_c_command",
|
1446 |
+
"group": "blimp",
|
1447 |
+
"dataset_path": "blimp",
|
1448 |
+
"dataset_name": "principle_A_c_command",
|
1449 |
+
"validation_split": "train",
|
1450 |
+
"doc_to_text": "",
|
1451 |
+
"doc_to_target": 0,
|
1452 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1453 |
+
"description": "",
|
1454 |
+
"target_delimiter": " ",
|
1455 |
+
"fewshot_delimiter": "\n\n",
|
1456 |
+
"num_fewshot": 0,
|
1457 |
+
"metric_list": [
|
1458 |
+
{
|
1459 |
+
"metric": "acc"
|
1460 |
+
}
|
1461 |
+
],
|
1462 |
+
"output_type": "multiple_choice",
|
1463 |
+
"repeats": 1,
|
1464 |
+
"should_decontaminate": true,
|
1465 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1466 |
+
"metadata": {
|
1467 |
+
"version": 1.0
|
1468 |
+
}
|
1469 |
+
},
|
1470 |
+
"blimp_principle_A_case_1": {
|
1471 |
+
"task": "blimp_principle_A_case_1",
|
1472 |
+
"group": "blimp",
|
1473 |
+
"dataset_path": "blimp",
|
1474 |
+
"dataset_name": "principle_A_case_1",
|
1475 |
+
"validation_split": "train",
|
1476 |
+
"doc_to_text": "",
|
1477 |
+
"doc_to_target": 0,
|
1478 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1479 |
+
"description": "",
|
1480 |
+
"target_delimiter": " ",
|
1481 |
+
"fewshot_delimiter": "\n\n",
|
1482 |
+
"num_fewshot": 0,
|
1483 |
+
"metric_list": [
|
1484 |
+
{
|
1485 |
+
"metric": "acc"
|
1486 |
+
}
|
1487 |
+
],
|
1488 |
+
"output_type": "multiple_choice",
|
1489 |
+
"repeats": 1,
|
1490 |
+
"should_decontaminate": true,
|
1491 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1492 |
+
"metadata": {
|
1493 |
+
"version": 1.0
|
1494 |
+
}
|
1495 |
+
},
|
1496 |
+
"blimp_principle_A_case_2": {
|
1497 |
+
"task": "blimp_principle_A_case_2",
|
1498 |
+
"group": "blimp",
|
1499 |
+
"dataset_path": "blimp",
|
1500 |
+
"dataset_name": "principle_A_case_2",
|
1501 |
+
"validation_split": "train",
|
1502 |
+
"doc_to_text": "",
|
1503 |
+
"doc_to_target": 0,
|
1504 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1505 |
+
"description": "",
|
1506 |
+
"target_delimiter": " ",
|
1507 |
+
"fewshot_delimiter": "\n\n",
|
1508 |
+
"num_fewshot": 0,
|
1509 |
+
"metric_list": [
|
1510 |
+
{
|
1511 |
+
"metric": "acc"
|
1512 |
+
}
|
1513 |
+
],
|
1514 |
+
"output_type": "multiple_choice",
|
1515 |
+
"repeats": 1,
|
1516 |
+
"should_decontaminate": true,
|
1517 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1518 |
+
"metadata": {
|
1519 |
+
"version": 1.0
|
1520 |
+
}
|
1521 |
+
},
|
1522 |
+
"blimp_principle_A_domain_1": {
|
1523 |
+
"task": "blimp_principle_A_domain_1",
|
1524 |
+
"group": "blimp",
|
1525 |
+
"dataset_path": "blimp",
|
1526 |
+
"dataset_name": "principle_A_domain_1",
|
1527 |
+
"validation_split": "train",
|
1528 |
+
"doc_to_text": "",
|
1529 |
+
"doc_to_target": 0,
|
1530 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1531 |
+
"description": "",
|
1532 |
+
"target_delimiter": " ",
|
1533 |
+
"fewshot_delimiter": "\n\n",
|
1534 |
+
"num_fewshot": 0,
|
1535 |
+
"metric_list": [
|
1536 |
+
{
|
1537 |
+
"metric": "acc"
|
1538 |
+
}
|
1539 |
+
],
|
1540 |
+
"output_type": "multiple_choice",
|
1541 |
+
"repeats": 1,
|
1542 |
+
"should_decontaminate": true,
|
1543 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1544 |
+
"metadata": {
|
1545 |
+
"version": 1.0
|
1546 |
+
}
|
1547 |
+
},
|
1548 |
+
"blimp_principle_A_domain_2": {
|
1549 |
+
"task": "blimp_principle_A_domain_2",
|
1550 |
+
"group": "blimp",
|
1551 |
+
"dataset_path": "blimp",
|
1552 |
+
"dataset_name": "principle_A_domain_2",
|
1553 |
+
"validation_split": "train",
|
1554 |
+
"doc_to_text": "",
|
1555 |
+
"doc_to_target": 0,
|
1556 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1557 |
+
"description": "",
|
1558 |
+
"target_delimiter": " ",
|
1559 |
+
"fewshot_delimiter": "\n\n",
|
1560 |
+
"num_fewshot": 0,
|
1561 |
+
"metric_list": [
|
1562 |
+
{
|
1563 |
+
"metric": "acc"
|
1564 |
+
}
|
1565 |
+
],
|
1566 |
+
"output_type": "multiple_choice",
|
1567 |
+
"repeats": 1,
|
1568 |
+
"should_decontaminate": true,
|
1569 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1570 |
+
"metadata": {
|
1571 |
+
"version": 1.0
|
1572 |
+
}
|
1573 |
+
},
|
1574 |
+
"blimp_principle_A_domain_3": {
|
1575 |
+
"task": "blimp_principle_A_domain_3",
|
1576 |
+
"group": "blimp",
|
1577 |
+
"dataset_path": "blimp",
|
1578 |
+
"dataset_name": "principle_A_domain_3",
|
1579 |
+
"validation_split": "train",
|
1580 |
+
"doc_to_text": "",
|
1581 |
+
"doc_to_target": 0,
|
1582 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1583 |
+
"description": "",
|
1584 |
+
"target_delimiter": " ",
|
1585 |
+
"fewshot_delimiter": "\n\n",
|
1586 |
+
"num_fewshot": 0,
|
1587 |
+
"metric_list": [
|
1588 |
+
{
|
1589 |
+
"metric": "acc"
|
1590 |
+
}
|
1591 |
+
],
|
1592 |
+
"output_type": "multiple_choice",
|
1593 |
+
"repeats": 1,
|
1594 |
+
"should_decontaminate": true,
|
1595 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1596 |
+
"metadata": {
|
1597 |
+
"version": 1.0
|
1598 |
+
}
|
1599 |
+
},
|
1600 |
+
"blimp_principle_A_reconstruction": {
|
1601 |
+
"task": "blimp_principle_A_reconstruction",
|
1602 |
+
"group": "blimp",
|
1603 |
+
"dataset_path": "blimp",
|
1604 |
+
"dataset_name": "principle_A_reconstruction",
|
1605 |
+
"validation_split": "train",
|
1606 |
+
"doc_to_text": "",
|
1607 |
+
"doc_to_target": 0,
|
1608 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1609 |
+
"description": "",
|
1610 |
+
"target_delimiter": " ",
|
1611 |
+
"fewshot_delimiter": "\n\n",
|
1612 |
+
"num_fewshot": 0,
|
1613 |
+
"metric_list": [
|
1614 |
+
{
|
1615 |
+
"metric": "acc"
|
1616 |
+
}
|
1617 |
+
],
|
1618 |
+
"output_type": "multiple_choice",
|
1619 |
+
"repeats": 1,
|
1620 |
+
"should_decontaminate": true,
|
1621 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1622 |
+
"metadata": {
|
1623 |
+
"version": 1.0
|
1624 |
+
}
|
1625 |
+
},
|
1626 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
1627 |
+
"task": "blimp_regular_plural_subject_verb_agreement_1",
|
1628 |
+
"group": "blimp",
|
1629 |
+
"dataset_path": "blimp",
|
1630 |
+
"dataset_name": "regular_plural_subject_verb_agreement_1",
|
1631 |
+
"validation_split": "train",
|
1632 |
+
"doc_to_text": "",
|
1633 |
+
"doc_to_target": 0,
|
1634 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1635 |
+
"description": "",
|
1636 |
+
"target_delimiter": " ",
|
1637 |
+
"fewshot_delimiter": "\n\n",
|
1638 |
+
"num_fewshot": 0,
|
1639 |
+
"metric_list": [
|
1640 |
+
{
|
1641 |
+
"metric": "acc"
|
1642 |
+
}
|
1643 |
+
],
|
1644 |
+
"output_type": "multiple_choice",
|
1645 |
+
"repeats": 1,
|
1646 |
+
"should_decontaminate": true,
|
1647 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1648 |
+
"metadata": {
|
1649 |
+
"version": 1.0
|
1650 |
+
}
|
1651 |
+
},
|
1652 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
1653 |
+
"task": "blimp_regular_plural_subject_verb_agreement_2",
|
1654 |
+
"group": "blimp",
|
1655 |
+
"dataset_path": "blimp",
|
1656 |
+
"dataset_name": "regular_plural_subject_verb_agreement_2",
|
1657 |
+
"validation_split": "train",
|
1658 |
+
"doc_to_text": "",
|
1659 |
+
"doc_to_target": 0,
|
1660 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1661 |
+
"description": "",
|
1662 |
+
"target_delimiter": " ",
|
1663 |
+
"fewshot_delimiter": "\n\n",
|
1664 |
+
"num_fewshot": 0,
|
1665 |
+
"metric_list": [
|
1666 |
+
{
|
1667 |
+
"metric": "acc"
|
1668 |
+
}
|
1669 |
+
],
|
1670 |
+
"output_type": "multiple_choice",
|
1671 |
+
"repeats": 1,
|
1672 |
+
"should_decontaminate": true,
|
1673 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1674 |
+
"metadata": {
|
1675 |
+
"version": 1.0
|
1676 |
+
}
|
1677 |
+
},
|
1678 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
1679 |
+
"task": "blimp_sentential_negation_npi_licensor_present",
|
1680 |
+
"group": "blimp",
|
1681 |
+
"dataset_path": "blimp",
|
1682 |
+
"dataset_name": "sentential_negation_npi_licensor_present",
|
1683 |
+
"validation_split": "train",
|
1684 |
+
"doc_to_text": "",
|
1685 |
+
"doc_to_target": 0,
|
1686 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1687 |
+
"description": "",
|
1688 |
+
"target_delimiter": " ",
|
1689 |
+
"fewshot_delimiter": "\n\n",
|
1690 |
+
"num_fewshot": 0,
|
1691 |
+
"metric_list": [
|
1692 |
+
{
|
1693 |
+
"metric": "acc"
|
1694 |
+
}
|
1695 |
+
],
|
1696 |
+
"output_type": "multiple_choice",
|
1697 |
+
"repeats": 1,
|
1698 |
+
"should_decontaminate": true,
|
1699 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1700 |
+
"metadata": {
|
1701 |
+
"version": 1.0
|
1702 |
+
}
|
1703 |
+
},
|
1704 |
+
"blimp_sentential_negation_npi_scope": {
|
1705 |
+
"task": "blimp_sentential_negation_npi_scope",
|
1706 |
+
"group": "blimp",
|
1707 |
+
"dataset_path": "blimp",
|
1708 |
+
"dataset_name": "sentential_negation_npi_scope",
|
1709 |
+
"validation_split": "train",
|
1710 |
+
"doc_to_text": "",
|
1711 |
+
"doc_to_target": 0,
|
1712 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1713 |
+
"description": "",
|
1714 |
+
"target_delimiter": " ",
|
1715 |
+
"fewshot_delimiter": "\n\n",
|
1716 |
+
"num_fewshot": 0,
|
1717 |
+
"metric_list": [
|
1718 |
+
{
|
1719 |
+
"metric": "acc"
|
1720 |
+
}
|
1721 |
+
],
|
1722 |
+
"output_type": "multiple_choice",
|
1723 |
+
"repeats": 1,
|
1724 |
+
"should_decontaminate": true,
|
1725 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1726 |
+
"metadata": {
|
1727 |
+
"version": 1.0
|
1728 |
+
}
|
1729 |
+
},
|
1730 |
+
"blimp_sentential_subject_island": {
|
1731 |
+
"task": "blimp_sentential_subject_island",
|
1732 |
+
"group": "blimp",
|
1733 |
+
"dataset_path": "blimp",
|
1734 |
+
"dataset_name": "sentential_subject_island",
|
1735 |
+
"validation_split": "train",
|
1736 |
+
"doc_to_text": "",
|
1737 |
+
"doc_to_target": 0,
|
1738 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1739 |
+
"description": "",
|
1740 |
+
"target_delimiter": " ",
|
1741 |
+
"fewshot_delimiter": "\n\n",
|
1742 |
+
"num_fewshot": 0,
|
1743 |
+
"metric_list": [
|
1744 |
+
{
|
1745 |
+
"metric": "acc"
|
1746 |
+
}
|
1747 |
+
],
|
1748 |
+
"output_type": "multiple_choice",
|
1749 |
+
"repeats": 1,
|
1750 |
+
"should_decontaminate": true,
|
1751 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1752 |
+
"metadata": {
|
1753 |
+
"version": 1.0
|
1754 |
+
}
|
1755 |
+
},
|
1756 |
+
"blimp_superlative_quantifiers_1": {
|
1757 |
+
"task": "blimp_superlative_quantifiers_1",
|
1758 |
+
"group": "blimp",
|
1759 |
+
"dataset_path": "blimp",
|
1760 |
+
"dataset_name": "superlative_quantifiers_1",
|
1761 |
+
"validation_split": "train",
|
1762 |
+
"doc_to_text": "",
|
1763 |
+
"doc_to_target": 0,
|
1764 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1765 |
+
"description": "",
|
1766 |
+
"target_delimiter": " ",
|
1767 |
+
"fewshot_delimiter": "\n\n",
|
1768 |
+
"num_fewshot": 0,
|
1769 |
+
"metric_list": [
|
1770 |
+
{
|
1771 |
+
"metric": "acc"
|
1772 |
+
}
|
1773 |
+
],
|
1774 |
+
"output_type": "multiple_choice",
|
1775 |
+
"repeats": 1,
|
1776 |
+
"should_decontaminate": true,
|
1777 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1778 |
+
"metadata": {
|
1779 |
+
"version": 1.0
|
1780 |
+
}
|
1781 |
+
},
|
1782 |
+
"blimp_superlative_quantifiers_2": {
|
1783 |
+
"task": "blimp_superlative_quantifiers_2",
|
1784 |
+
"group": "blimp",
|
1785 |
+
"dataset_path": "blimp",
|
1786 |
+
"dataset_name": "superlative_quantifiers_2",
|
1787 |
+
"validation_split": "train",
|
1788 |
+
"doc_to_text": "",
|
1789 |
+
"doc_to_target": 0,
|
1790 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1791 |
+
"description": "",
|
1792 |
+
"target_delimiter": " ",
|
1793 |
+
"fewshot_delimiter": "\n\n",
|
1794 |
+
"num_fewshot": 0,
|
1795 |
+
"metric_list": [
|
1796 |
+
{
|
1797 |
+
"metric": "acc"
|
1798 |
+
}
|
1799 |
+
],
|
1800 |
+
"output_type": "multiple_choice",
|
1801 |
+
"repeats": 1,
|
1802 |
+
"should_decontaminate": true,
|
1803 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1804 |
+
"metadata": {
|
1805 |
+
"version": 1.0
|
1806 |
+
}
|
1807 |
+
},
|
1808 |
+
"blimp_tough_vs_raising_1": {
|
1809 |
+
"task": "blimp_tough_vs_raising_1",
|
1810 |
+
"group": "blimp",
|
1811 |
+
"dataset_path": "blimp",
|
1812 |
+
"dataset_name": "tough_vs_raising_1",
|
1813 |
+
"validation_split": "train",
|
1814 |
+
"doc_to_text": "",
|
1815 |
+
"doc_to_target": 0,
|
1816 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1817 |
+
"description": "",
|
1818 |
+
"target_delimiter": " ",
|
1819 |
+
"fewshot_delimiter": "\n\n",
|
1820 |
+
"num_fewshot": 0,
|
1821 |
+
"metric_list": [
|
1822 |
+
{
|
1823 |
+
"metric": "acc"
|
1824 |
+
}
|
1825 |
+
],
|
1826 |
+
"output_type": "multiple_choice",
|
1827 |
+
"repeats": 1,
|
1828 |
+
"should_decontaminate": true,
|
1829 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1830 |
+
"metadata": {
|
1831 |
+
"version": 1.0
|
1832 |
+
}
|
1833 |
+
},
|
1834 |
+
"blimp_tough_vs_raising_2": {
|
1835 |
+
"task": "blimp_tough_vs_raising_2",
|
1836 |
+
"group": "blimp",
|
1837 |
+
"dataset_path": "blimp",
|
1838 |
+
"dataset_name": "tough_vs_raising_2",
|
1839 |
+
"validation_split": "train",
|
1840 |
+
"doc_to_text": "",
|
1841 |
+
"doc_to_target": 0,
|
1842 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1843 |
+
"description": "",
|
1844 |
+
"target_delimiter": " ",
|
1845 |
+
"fewshot_delimiter": "\n\n",
|
1846 |
+
"num_fewshot": 0,
|
1847 |
+
"metric_list": [
|
1848 |
+
{
|
1849 |
+
"metric": "acc"
|
1850 |
+
}
|
1851 |
+
],
|
1852 |
+
"output_type": "multiple_choice",
|
1853 |
+
"repeats": 1,
|
1854 |
+
"should_decontaminate": true,
|
1855 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1856 |
+
"metadata": {
|
1857 |
+
"version": 1.0
|
1858 |
+
}
|
1859 |
+
},
|
1860 |
+
"blimp_transitive": {
|
1861 |
+
"task": "blimp_transitive",
|
1862 |
+
"group": "blimp",
|
1863 |
+
"dataset_path": "blimp",
|
1864 |
+
"dataset_name": "transitive",
|
1865 |
+
"validation_split": "train",
|
1866 |
+
"doc_to_text": "",
|
1867 |
+
"doc_to_target": 0,
|
1868 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1869 |
+
"description": "",
|
1870 |
+
"target_delimiter": " ",
|
1871 |
+
"fewshot_delimiter": "\n\n",
|
1872 |
+
"num_fewshot": 0,
|
1873 |
+
"metric_list": [
|
1874 |
+
{
|
1875 |
+
"metric": "acc"
|
1876 |
+
}
|
1877 |
+
],
|
1878 |
+
"output_type": "multiple_choice",
|
1879 |
+
"repeats": 1,
|
1880 |
+
"should_decontaminate": true,
|
1881 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1882 |
+
"metadata": {
|
1883 |
+
"version": 1.0
|
1884 |
+
}
|
1885 |
+
},
|
1886 |
+
"blimp_wh_island": {
|
1887 |
+
"task": "blimp_wh_island",
|
1888 |
+
"group": "blimp",
|
1889 |
+
"dataset_path": "blimp",
|
1890 |
+
"dataset_name": "wh_island",
|
1891 |
+
"validation_split": "train",
|
1892 |
+
"doc_to_text": "",
|
1893 |
+
"doc_to_target": 0,
|
1894 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1895 |
+
"description": "",
|
1896 |
+
"target_delimiter": " ",
|
1897 |
+
"fewshot_delimiter": "\n\n",
|
1898 |
+
"num_fewshot": 0,
|
1899 |
+
"metric_list": [
|
1900 |
+
{
|
1901 |
+
"metric": "acc"
|
1902 |
+
}
|
1903 |
+
],
|
1904 |
+
"output_type": "multiple_choice",
|
1905 |
+
"repeats": 1,
|
1906 |
+
"should_decontaminate": true,
|
1907 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1908 |
+
"metadata": {
|
1909 |
+
"version": 1.0
|
1910 |
+
}
|
1911 |
+
},
|
1912 |
+
"blimp_wh_questions_object_gap": {
|
1913 |
+
"task": "blimp_wh_questions_object_gap",
|
1914 |
+
"group": "blimp",
|
1915 |
+
"dataset_path": "blimp",
|
1916 |
+
"dataset_name": "wh_questions_object_gap",
|
1917 |
+
"validation_split": "train",
|
1918 |
+
"doc_to_text": "",
|
1919 |
+
"doc_to_target": 0,
|
1920 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1921 |
+
"description": "",
|
1922 |
+
"target_delimiter": " ",
|
1923 |
+
"fewshot_delimiter": "\n\n",
|
1924 |
+
"num_fewshot": 0,
|
1925 |
+
"metric_list": [
|
1926 |
+
{
|
1927 |
+
"metric": "acc"
|
1928 |
+
}
|
1929 |
+
],
|
1930 |
+
"output_type": "multiple_choice",
|
1931 |
+
"repeats": 1,
|
1932 |
+
"should_decontaminate": true,
|
1933 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1934 |
+
"metadata": {
|
1935 |
+
"version": 1.0
|
1936 |
+
}
|
1937 |
+
},
|
1938 |
+
"blimp_wh_questions_subject_gap": {
|
1939 |
+
"task": "blimp_wh_questions_subject_gap",
|
1940 |
+
"group": "blimp",
|
1941 |
+
"dataset_path": "blimp",
|
1942 |
+
"dataset_name": "wh_questions_subject_gap",
|
1943 |
+
"validation_split": "train",
|
1944 |
+
"doc_to_text": "",
|
1945 |
+
"doc_to_target": 0,
|
1946 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1947 |
+
"description": "",
|
1948 |
+
"target_delimiter": " ",
|
1949 |
+
"fewshot_delimiter": "\n\n",
|
1950 |
+
"num_fewshot": 0,
|
1951 |
+
"metric_list": [
|
1952 |
+
{
|
1953 |
+
"metric": "acc"
|
1954 |
+
}
|
1955 |
+
],
|
1956 |
+
"output_type": "multiple_choice",
|
1957 |
+
"repeats": 1,
|
1958 |
+
"should_decontaminate": true,
|
1959 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1960 |
+
"metadata": {
|
1961 |
+
"version": 1.0
|
1962 |
+
}
|
1963 |
+
},
|
1964 |
+
"blimp_wh_questions_subject_gap_long_distance": {
|
1965 |
+
"task": "blimp_wh_questions_subject_gap_long_distance",
|
1966 |
+
"group": "blimp",
|
1967 |
+
"dataset_path": "blimp",
|
1968 |
+
"dataset_name": "wh_questions_subject_gap_long_distance",
|
1969 |
+
"validation_split": "train",
|
1970 |
+
"doc_to_text": "",
|
1971 |
+
"doc_to_target": 0,
|
1972 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1973 |
+
"description": "",
|
1974 |
+
"target_delimiter": " ",
|
1975 |
+
"fewshot_delimiter": "\n\n",
|
1976 |
+
"num_fewshot": 0,
|
1977 |
+
"metric_list": [
|
1978 |
+
{
|
1979 |
+
"metric": "acc"
|
1980 |
+
}
|
1981 |
+
],
|
1982 |
+
"output_type": "multiple_choice",
|
1983 |
+
"repeats": 1,
|
1984 |
+
"should_decontaminate": true,
|
1985 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
1986 |
+
"metadata": {
|
1987 |
+
"version": 1.0
|
1988 |
+
}
|
1989 |
+
},
|
1990 |
+
"blimp_wh_vs_that_no_gap": {
|
1991 |
+
"task": "blimp_wh_vs_that_no_gap",
|
1992 |
+
"group": "blimp",
|
1993 |
+
"dataset_path": "blimp",
|
1994 |
+
"dataset_name": "wh_vs_that_no_gap",
|
1995 |
+
"validation_split": "train",
|
1996 |
+
"doc_to_text": "",
|
1997 |
+
"doc_to_target": 0,
|
1998 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
1999 |
+
"description": "",
|
2000 |
+
"target_delimiter": " ",
|
2001 |
+
"fewshot_delimiter": "\n\n",
|
2002 |
+
"num_fewshot": 0,
|
2003 |
+
"metric_list": [
|
2004 |
+
{
|
2005 |
+
"metric": "acc"
|
2006 |
+
}
|
2007 |
+
],
|
2008 |
+
"output_type": "multiple_choice",
|
2009 |
+
"repeats": 1,
|
2010 |
+
"should_decontaminate": true,
|
2011 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2012 |
+
"metadata": {
|
2013 |
+
"version": 1.0
|
2014 |
+
}
|
2015 |
+
},
|
2016 |
+
"blimp_wh_vs_that_no_gap_long_distance": {
|
2017 |
+
"task": "blimp_wh_vs_that_no_gap_long_distance",
|
2018 |
+
"group": "blimp",
|
2019 |
+
"dataset_path": "blimp",
|
2020 |
+
"dataset_name": "wh_vs_that_no_gap_long_distance",
|
2021 |
+
"validation_split": "train",
|
2022 |
+
"doc_to_text": "",
|
2023 |
+
"doc_to_target": 0,
|
2024 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2025 |
+
"description": "",
|
2026 |
+
"target_delimiter": " ",
|
2027 |
+
"fewshot_delimiter": "\n\n",
|
2028 |
+
"num_fewshot": 0,
|
2029 |
+
"metric_list": [
|
2030 |
+
{
|
2031 |
+
"metric": "acc"
|
2032 |
+
}
|
2033 |
+
],
|
2034 |
+
"output_type": "multiple_choice",
|
2035 |
+
"repeats": 1,
|
2036 |
+
"should_decontaminate": true,
|
2037 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2038 |
+
"metadata": {
|
2039 |
+
"version": 1.0
|
2040 |
+
}
|
2041 |
+
},
|
2042 |
+
"blimp_wh_vs_that_with_gap": {
|
2043 |
+
"task": "blimp_wh_vs_that_with_gap",
|
2044 |
+
"group": "blimp",
|
2045 |
+
"dataset_path": "blimp",
|
2046 |
+
"dataset_name": "wh_vs_that_with_gap",
|
2047 |
+
"validation_split": "train",
|
2048 |
+
"doc_to_text": "",
|
2049 |
+
"doc_to_target": 0,
|
2050 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2051 |
+
"description": "",
|
2052 |
+
"target_delimiter": " ",
|
2053 |
+
"fewshot_delimiter": "\n\n",
|
2054 |
+
"num_fewshot": 0,
|
2055 |
+
"metric_list": [
|
2056 |
+
{
|
2057 |
+
"metric": "acc"
|
2058 |
+
}
|
2059 |
+
],
|
2060 |
+
"output_type": "multiple_choice",
|
2061 |
+
"repeats": 1,
|
2062 |
+
"should_decontaminate": true,
|
2063 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2064 |
+
"metadata": {
|
2065 |
+
"version": 1.0
|
2066 |
+
}
|
2067 |
+
},
|
2068 |
+
"blimp_wh_vs_that_with_gap_long_distance": {
|
2069 |
+
"task": "blimp_wh_vs_that_with_gap_long_distance",
|
2070 |
+
"group": "blimp",
|
2071 |
+
"dataset_path": "blimp",
|
2072 |
+
"dataset_name": "wh_vs_that_with_gap_long_distance",
|
2073 |
+
"validation_split": "train",
|
2074 |
+
"doc_to_text": "",
|
2075 |
+
"doc_to_target": 0,
|
2076 |
+
"doc_to_choice": "{{[sentence_good, sentence_bad]}}",
|
2077 |
+
"description": "",
|
2078 |
+
"target_delimiter": " ",
|
2079 |
+
"fewshot_delimiter": "\n\n",
|
2080 |
+
"num_fewshot": 0,
|
2081 |
+
"metric_list": [
|
2082 |
+
{
|
2083 |
+
"metric": "acc"
|
2084 |
+
}
|
2085 |
+
],
|
2086 |
+
"output_type": "multiple_choice",
|
2087 |
+
"repeats": 1,
|
2088 |
+
"should_decontaminate": true,
|
2089 |
+
"doc_to_decontamination_query": "{{sentence_good}} {{sentence_bad}}",
|
2090 |
+
"metadata": {
|
2091 |
+
"version": 1.0
|
2092 |
+
}
|
2093 |
+
}
|
2094 |
+
},
|
2095 |
+
"versions": {
|
2096 |
+
"blimp": "N/A",
|
2097 |
+
"blimp_adjunct_island": 1.0,
|
2098 |
+
"blimp_anaphor_gender_agreement": 1.0,
|
2099 |
+
"blimp_anaphor_number_agreement": 1.0,
|
2100 |
+
"blimp_animate_subject_passive": 1.0,
|
2101 |
+
"blimp_animate_subject_trans": 1.0,
|
2102 |
+
"blimp_causative": 1.0,
|
2103 |
+
"blimp_complex_NP_island": 1.0,
|
2104 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": 1.0,
|
2105 |
+
"blimp_coordinate_structure_constraint_object_extraction": 1.0,
|
2106 |
+
"blimp_determiner_noun_agreement_1": 1.0,
|
2107 |
+
"blimp_determiner_noun_agreement_2": 1.0,
|
2108 |
+
"blimp_determiner_noun_agreement_irregular_1": 1.0,
|
2109 |
+
"blimp_determiner_noun_agreement_irregular_2": 1.0,
|
2110 |
+
"blimp_determiner_noun_agreement_with_adj_2": 1.0,
|
2111 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": 1.0,
|
2112 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": 1.0,
|
2113 |
+
"blimp_determiner_noun_agreement_with_adjective_1": 1.0,
|
2114 |
+
"blimp_distractor_agreement_relational_noun": 1.0,
|
2115 |
+
"blimp_distractor_agreement_relative_clause": 1.0,
|
2116 |
+
"blimp_drop_argument": 1.0,
|
2117 |
+
"blimp_ellipsis_n_bar_1": 1.0,
|
2118 |
+
"blimp_ellipsis_n_bar_2": 1.0,
|
2119 |
+
"blimp_existential_there_object_raising": 1.0,
|
2120 |
+
"blimp_existential_there_quantifiers_1": 1.0,
|
2121 |
+
"blimp_existential_there_quantifiers_2": 1.0,
|
2122 |
+
"blimp_existential_there_subject_raising": 1.0,
|
2123 |
+
"blimp_expletive_it_object_raising": 1.0,
|
2124 |
+
"blimp_inchoative": 1.0,
|
2125 |
+
"blimp_intransitive": 1.0,
|
2126 |
+
"blimp_irregular_past_participle_adjectives": 1.0,
|
2127 |
+
"blimp_irregular_past_participle_verbs": 1.0,
|
2128 |
+
"blimp_irregular_plural_subject_verb_agreement_1": 1.0,
|
2129 |
+
"blimp_irregular_plural_subject_verb_agreement_2": 1.0,
|
2130 |
+
"blimp_left_branch_island_echo_question": 1.0,
|
2131 |
+
"blimp_left_branch_island_simple_question": 1.0,
|
2132 |
+
"blimp_matrix_question_npi_licensor_present": 1.0,
|
2133 |
+
"blimp_npi_present_1": 1.0,
|
2134 |
+
"blimp_npi_present_2": 1.0,
|
2135 |
+
"blimp_only_npi_licensor_present": 1.0,
|
2136 |
+
"blimp_only_npi_scope": 1.0,
|
2137 |
+
"blimp_passive_1": 1.0,
|
2138 |
+
"blimp_passive_2": 1.0,
|
2139 |
+
"blimp_principle_A_c_command": 1.0,
|
2140 |
+
"blimp_principle_A_case_1": 1.0,
|
2141 |
+
"blimp_principle_A_case_2": 1.0,
|
2142 |
+
"blimp_principle_A_domain_1": 1.0,
|
2143 |
+
"blimp_principle_A_domain_2": 1.0,
|
2144 |
+
"blimp_principle_A_domain_3": 1.0,
|
2145 |
+
"blimp_principle_A_reconstruction": 1.0,
|
2146 |
+
"blimp_regular_plural_subject_verb_agreement_1": 1.0,
|
2147 |
+
"blimp_regular_plural_subject_verb_agreement_2": 1.0,
|
2148 |
+
"blimp_sentential_negation_npi_licensor_present": 1.0,
|
2149 |
+
"blimp_sentential_negation_npi_scope": 1.0,
|
2150 |
+
"blimp_sentential_subject_island": 1.0,
|
2151 |
+
"blimp_superlative_quantifiers_1": 1.0,
|
2152 |
+
"blimp_superlative_quantifiers_2": 1.0,
|
2153 |
+
"blimp_tough_vs_raising_1": 1.0,
|
2154 |
+
"blimp_tough_vs_raising_2": 1.0,
|
2155 |
+
"blimp_transitive": 1.0,
|
2156 |
+
"blimp_wh_island": 1.0,
|
2157 |
+
"blimp_wh_questions_object_gap": 1.0,
|
2158 |
+
"blimp_wh_questions_subject_gap": 1.0,
|
2159 |
+
"blimp_wh_questions_subject_gap_long_distance": 1.0,
|
2160 |
+
"blimp_wh_vs_that_no_gap": 1.0,
|
2161 |
+
"blimp_wh_vs_that_no_gap_long_distance": 1.0,
|
2162 |
+
"blimp_wh_vs_that_with_gap": 1.0,
|
2163 |
+
"blimp_wh_vs_that_with_gap_long_distance": 1.0
|
2164 |
+
},
|
2165 |
+
"n-shot": {
|
2166 |
+
"blimp": 0,
|
2167 |
+
"blimp_adjunct_island": 0,
|
2168 |
+
"blimp_anaphor_gender_agreement": 0,
|
2169 |
+
"blimp_anaphor_number_agreement": 0,
|
2170 |
+
"blimp_animate_subject_passive": 0,
|
2171 |
+
"blimp_animate_subject_trans": 0,
|
2172 |
+
"blimp_causative": 0,
|
2173 |
+
"blimp_complex_NP_island": 0,
|
2174 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": 0,
|
2175 |
+
"blimp_coordinate_structure_constraint_object_extraction": 0,
|
2176 |
+
"blimp_determiner_noun_agreement_1": 0,
|
2177 |
+
"blimp_determiner_noun_agreement_2": 0,
|
2178 |
+
"blimp_determiner_noun_agreement_irregular_1": 0,
|
2179 |
+
"blimp_determiner_noun_agreement_irregular_2": 0,
|
2180 |
+
"blimp_determiner_noun_agreement_with_adj_2": 0,
|
2181 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": 0,
|
2182 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": 0,
|
2183 |
+
"blimp_determiner_noun_agreement_with_adjective_1": 0,
|
2184 |
+
"blimp_distractor_agreement_relational_noun": 0,
|
2185 |
+
"blimp_distractor_agreement_relative_clause": 0,
|
2186 |
+
"blimp_drop_argument": 0,
|
2187 |
+
"blimp_ellipsis_n_bar_1": 0,
|
2188 |
+
"blimp_ellipsis_n_bar_2": 0,
|
2189 |
+
"blimp_existential_there_object_raising": 0,
|
2190 |
+
"blimp_existential_there_quantifiers_1": 0,
|
2191 |
+
"blimp_existential_there_quantifiers_2": 0,
|
2192 |
+
"blimp_existential_there_subject_raising": 0,
|
2193 |
+
"blimp_expletive_it_object_raising": 0,
|
2194 |
+
"blimp_inchoative": 0,
|
2195 |
+
"blimp_intransitive": 0,
|
2196 |
+
"blimp_irregular_past_participle_adjectives": 0,
|
2197 |
+
"blimp_irregular_past_participle_verbs": 0,
|
2198 |
+
"blimp_irregular_plural_subject_verb_agreement_1": 0,
|
2199 |
+
"blimp_irregular_plural_subject_verb_agreement_2": 0,
|
2200 |
+
"blimp_left_branch_island_echo_question": 0,
|
2201 |
+
"blimp_left_branch_island_simple_question": 0,
|
2202 |
+
"blimp_matrix_question_npi_licensor_present": 0,
|
2203 |
+
"blimp_npi_present_1": 0,
|
2204 |
+
"blimp_npi_present_2": 0,
|
2205 |
+
"blimp_only_npi_licensor_present": 0,
|
2206 |
+
"blimp_only_npi_scope": 0,
|
2207 |
+
"blimp_passive_1": 0,
|
2208 |
+
"blimp_passive_2": 0,
|
2209 |
+
"blimp_principle_A_c_command": 0,
|
2210 |
+
"blimp_principle_A_case_1": 0,
|
2211 |
+
"blimp_principle_A_case_2": 0,
|
2212 |
+
"blimp_principle_A_domain_1": 0,
|
2213 |
+
"blimp_principle_A_domain_2": 0,
|
2214 |
+
"blimp_principle_A_domain_3": 0,
|
2215 |
+
"blimp_principle_A_reconstruction": 0,
|
2216 |
+
"blimp_regular_plural_subject_verb_agreement_1": 0,
|
2217 |
+
"blimp_regular_plural_subject_verb_agreement_2": 0,
|
2218 |
+
"blimp_sentential_negation_npi_licensor_present": 0,
|
2219 |
+
"blimp_sentential_negation_npi_scope": 0,
|
2220 |
+
"blimp_sentential_subject_island": 0,
|
2221 |
+
"blimp_superlative_quantifiers_1": 0,
|
2222 |
+
"blimp_superlative_quantifiers_2": 0,
|
2223 |
+
"blimp_tough_vs_raising_1": 0,
|
2224 |
+
"blimp_tough_vs_raising_2": 0,
|
2225 |
+
"blimp_transitive": 0,
|
2226 |
+
"blimp_wh_island": 0,
|
2227 |
+
"blimp_wh_questions_object_gap": 0,
|
2228 |
+
"blimp_wh_questions_subject_gap": 0,
|
2229 |
+
"blimp_wh_questions_subject_gap_long_distance": 0,
|
2230 |
+
"blimp_wh_vs_that_no_gap": 0,
|
2231 |
+
"blimp_wh_vs_that_no_gap_long_distance": 0,
|
2232 |
+
"blimp_wh_vs_that_with_gap": 0,
|
2233 |
+
"blimp_wh_vs_that_with_gap_long_distance": 0
|
2234 |
+
},
|
2235 |
+
"config": {
|
2236 |
+
"model": "hf",
|
2237 |
+
"model_args": "pretrained=SmerkyG/rwkv-5-world-3b,dtype=bfloat16,trust_remote_code=True",
|
2238 |
+
"batch_size": "auto",
|
2239 |
+
"batch_sizes": [
|
2240 |
+
64
|
2241 |
+
],
|
2242 |
+
"device": null,
|
2243 |
+
"use_cache": null,
|
2244 |
+
"limit": null,
|
2245 |
+
"bootstrap_iters": 100000,
|
2246 |
+
"gen_kwargs": null
|
2247 |
+
},
|
2248 |
+
"git_hash": "1ee41f7"
|
2249 |
+
}
|
lm-eval-output/SmerkyG/rwkv-5-world-3b/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:718ff43719a07f9fc3f778b7c693026ca84630e3ea716921ab0cc81e2d9ef78d
|
3 |
+
size 325657
|