Oskar Douwe van der Wal
commited on
Commit
·
2ef66eb
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Parent(s):
4e806d3
New results
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- pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-07-05T13-24-18.246759.json.bak +2606 -0
- pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-07-11T13-45-02.042989.json.bak +2606 -0
- pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-34-26.878314.json +482 -0
- pythia-14m-seed1/step1/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-38-36.798754.json +482 -0
- pythia-14m-seed1/step1000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-20-43.708459.json +482 -0
- pythia-14m-seed1/step10000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-58-13.399858.json +482 -0
- pythia-14m-seed1/step11000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-02-19.860338.json +482 -0
- pythia-14m-seed1/step12000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-06-26.034288.json +482 -0
- pythia-14m-seed1/step128/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-08-24.350787.json +482 -0
- pythia-14m-seed1/step13000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-10-32.725397.json +482 -0
- pythia-14m-seed1/step14000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-14-36.870500.json +482 -0
- pythia-14m-seed1/step15000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-18-44.185809.json +482 -0
- pythia-14m-seed1/step16/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-55-59.794548.json +482 -0
- pythia-14m-seed1/step16000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-22-52.656419.json +482 -0
- pythia-14m-seed1/step17000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-27-02.544847.json +482 -0
- pythia-14m-seed1/step18000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-31-07.685214.json +482 -0
- pythia-14m-seed1/step19000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-35-11.859545.json +482 -0
- pythia-14m-seed1/step2/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-42-57.791663.json +482 -0
- pythia-14m-seed1/step2000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-25-00.074484.json +482 -0
- pythia-14m-seed1/step20000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-39-21.895446.json +482 -0
- pythia-14m-seed1/step21000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-43-30.344693.json +482 -0
- pythia-14m-seed1/step22000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-47-38.819675.json +482 -0
- pythia-14m-seed1/step23000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-51-49.064750.json +482 -0
- pythia-14m-seed1/step24000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-55-57.280297.json +482 -0
- pythia-14m-seed1/step25000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-00-23.273296.json +482 -0
- pythia-14m-seed1/step256/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-12-30.518046.json +482 -0
- pythia-14m-seed1/step26000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-04-43.508436.json +482 -0
- pythia-14m-seed1/step27000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-08-54.479591.json +482 -0
- pythia-14m-seed1/step28000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-13-03.437554.json +482 -0
- pythia-14m-seed1/step29000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-17-10.235976.json +482 -0
- pythia-14m-seed1/step3000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-29-07.176670.json +482 -0
- pythia-14m-seed1/step30000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-21-21.161950.json +482 -0
- pythia-14m-seed1/step31000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-25-31.758593.json +482 -0
- pythia-14m-seed1/step32/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-00-10.088125.json +482 -0
- pythia-14m-seed1/step32000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-29-41.037818.json +482 -0
- pythia-14m-seed1/step33000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-33-49.879234.json +482 -0
- pythia-14m-seed1/step34000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-38-10.348821.json +482 -0
- pythia-14m-seed1/step35000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-42-22.390553.json +482 -0
- pythia-14m-seed1/step36000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-46-35.988527.json +482 -0
- pythia-14m-seed1/step37000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-50-44.879723.json +482 -0
- pythia-14m-seed1/step38000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-54-53.146841.json +482 -0
- pythia-14m-seed1/step39000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-59-06.449694.json +482 -0
- pythia-14m-seed1/step4/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-47-23.908503.json +482 -0
- pythia-14m-seed1/step4000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-33-15.998610.json +482 -0
- pythia-14m-seed1/step40000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-03-22.077168.json +482 -0
- pythia-14m-seed1/step41000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-07-29.805237.json +482 -0
- pythia-14m-seed1/step42000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-11-40.178060.json +482 -0
- pythia-14m-seed1/step43000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-15-48.237659.json +482 -0
- pythia-14m-seed1/step44000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-20-11.460936.json +482 -0
- pythia-14m-seed1/step45000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-24-41.702741.json +482 -0
pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-07-05T13-24-18.246759.json.bak
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"blimp": {
|
4 |
+
"acc,none": 0.5175074626865671,
|
5 |
+
"acc_stderr,none": 0.001882002716920592,
|
6 |
+
"alias": "blimp"
|
7 |
+
},
|
8 |
+
"blimp_adjunct_island": {
|
9 |
+
"acc,none": 0.56,
|
10 |
+
"acc_stderr,none": 0.015704987954361718,
|
11 |
+
"alias": " - blimp_adjunct_island"
|
12 |
+
},
|
13 |
+
"blimp_anaphor_gender_agreement": {
|
14 |
+
"acc,none": 0.558,
|
15 |
+
"acc_stderr,none": 0.01571250721186415,
|
16 |
+
"alias": " - blimp_anaphor_gender_agreement"
|
17 |
+
},
|
18 |
+
"blimp_anaphor_number_agreement": {
|
19 |
+
"acc,none": 0.539,
|
20 |
+
"acc_stderr,none": 0.015771104201283047,
|
21 |
+
"alias": " - blimp_anaphor_number_agreement"
|
22 |
+
},
|
23 |
+
"blimp_animate_subject_passive": {
|
24 |
+
"acc,none": 0.593,
|
25 |
+
"acc_stderr,none": 0.015543249100255474,
|
26 |
+
"alias": " - blimp_animate_subject_passive"
|
27 |
+
},
|
28 |
+
"blimp_animate_subject_trans": {
|
29 |
+
"acc,none": 0.813,
|
30 |
+
"acc_stderr,none": 0.012336254828074168,
|
31 |
+
"alias": " - blimp_animate_subject_trans"
|
32 |
+
},
|
33 |
+
"blimp_causative": {
|
34 |
+
"acc,none": 0.457,
|
35 |
+
"acc_stderr,none": 0.0157606915901365,
|
36 |
+
"alias": " - blimp_causative"
|
37 |
+
},
|
38 |
+
"blimp_complex_NP_island": {
|
39 |
+
"acc,none": 0.509,
|
40 |
+
"acc_stderr,none": 0.0158167369950053,
|
41 |
+
"alias": " - blimp_complex_NP_island"
|
42 |
+
},
|
43 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
44 |
+
"acc,none": 0.545,
|
45 |
+
"acc_stderr,none": 0.015755101498347232,
|
46 |
+
"alias": " - blimp_coordinate_structure_constraint_complex_left_branch"
|
47 |
+
},
|
48 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
49 |
+
"acc,none": 0.531,
|
50 |
+
"acc_stderr,none": 0.015788865959538965,
|
51 |
+
"alias": " - blimp_coordinate_structure_constraint_object_extraction"
|
52 |
+
},
|
53 |
+
"blimp_determiner_noun_agreement_1": {
|
54 |
+
"acc,none": 0.538,
|
55 |
+
"acc_stderr,none": 0.015773547629015002,
|
56 |
+
"alias": " - blimp_determiner_noun_agreement_1"
|
57 |
+
},
|
58 |
+
"blimp_determiner_noun_agreement_2": {
|
59 |
+
"acc,none": 0.53,
|
60 |
+
"acc_stderr,none": 0.015790799515836725,
|
61 |
+
"alias": " - blimp_determiner_noun_agreement_2"
|
62 |
+
},
|
63 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
64 |
+
"acc,none": 0.498,
|
65 |
+
"acc_stderr,none": 0.01581917337430266,
|
66 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_1"
|
67 |
+
},
|
68 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
69 |
+
"acc,none": 0.471,
|
70 |
+
"acc_stderr,none": 0.015792669451628764,
|
71 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_2"
|
72 |
+
},
|
73 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
74 |
+
"acc,none": 0.538,
|
75 |
+
"acc_stderr,none": 0.015773547629015002,
|
76 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_2"
|
77 |
+
},
|
78 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
79 |
+
"acc,none": 0.54,
|
80 |
+
"acc_stderr,none": 0.0157685969143944,
|
81 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_1"
|
82 |
+
},
|
83 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
84 |
+
"acc,none": 0.525,
|
85 |
+
"acc_stderr,none": 0.015799513429996023,
|
86 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_2"
|
87 |
+
},
|
88 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
89 |
+
"acc,none": 0.499,
|
90 |
+
"acc_stderr,none": 0.015819268290576817,
|
91 |
+
"alias": " - blimp_determiner_noun_agreement_with_adjective_1"
|
92 |
+
},
|
93 |
+
"blimp_distractor_agreement_relational_noun": {
|
94 |
+
"acc,none": 0.477,
|
95 |
+
"acc_stderr,none": 0.015802554246726087,
|
96 |
+
"alias": " - blimp_distractor_agreement_relational_noun"
|
97 |
+
},
|
98 |
+
"blimp_distractor_agreement_relative_clause": {
|
99 |
+
"acc,none": 0.511,
|
100 |
+
"acc_stderr,none": 0.01581547119529257,
|
101 |
+
"alias": " - blimp_distractor_agreement_relative_clause"
|
102 |
+
},
|
103 |
+
"blimp_drop_argument": {
|
104 |
+
"acc,none": 0.663,
|
105 |
+
"acc_stderr,none": 0.01495508791865346,
|
106 |
+
"alias": " - blimp_drop_argument"
|
107 |
+
},
|
108 |
+
"blimp_ellipsis_n_bar_1": {
|
109 |
+
"acc,none": 0.431,
|
110 |
+
"acc_stderr,none": 0.01566794448817344,
|
111 |
+
"alias": " - blimp_ellipsis_n_bar_1"
|
112 |
+
},
|
113 |
+
"blimp_ellipsis_n_bar_2": {
|
114 |
+
"acc,none": 0.327,
|
115 |
+
"acc_stderr,none": 0.014842213153411162,
|
116 |
+
"alias": " - blimp_ellipsis_n_bar_2"
|
117 |
+
},
|
118 |
+
"blimp_existential_there_object_raising": {
|
119 |
+
"acc,none": 0.69,
|
120 |
+
"acc_stderr,none": 0.014632638658632766,
|
121 |
+
"alias": " - blimp_existential_there_object_raising"
|
122 |
+
},
|
123 |
+
"blimp_existential_there_quantifiers_1": {
|
124 |
+
"acc,none": 0.483,
|
125 |
+
"acc_stderr,none": 0.015810153729833274,
|
126 |
+
"alias": " - blimp_existential_there_quantifiers_1"
|
127 |
+
},
|
128 |
+
"blimp_existential_there_quantifiers_2": {
|
129 |
+
"acc,none": 0.833,
|
130 |
+
"acc_stderr,none": 0.011800434324644593,
|
131 |
+
"alias": " - blimp_existential_there_quantifiers_2"
|
132 |
+
},
|
133 |
+
"blimp_existential_there_subject_raising": {
|
134 |
+
"acc,none": 0.576,
|
135 |
+
"acc_stderr,none": 0.015635487471405186,
|
136 |
+
"alias": " - blimp_existential_there_subject_raising"
|
137 |
+
},
|
138 |
+
"blimp_expletive_it_object_raising": {
|
139 |
+
"acc,none": 0.589,
|
140 |
+
"acc_stderr,none": 0.01556667341859915,
|
141 |
+
"alias": " - blimp_expletive_it_object_raising"
|
142 |
+
},
|
143 |
+
"blimp_inchoative": {
|
144 |
+
"acc,none": 0.421,
|
145 |
+
"acc_stderr,none": 0.015620595475301285,
|
146 |
+
"alias": " - blimp_inchoative"
|
147 |
+
},
|
148 |
+
"blimp_intransitive": {
|
149 |
+
"acc,none": 0.554,
|
150 |
+
"acc_stderr,none": 0.01572677116675039,
|
151 |
+
"alias": " - blimp_intransitive"
|
152 |
+
},
|
153 |
+
"blimp_irregular_past_participle_adjectives": {
|
154 |
+
"acc,none": 0.533,
|
155 |
+
"acc_stderr,none": 0.015784807891138876,
|
156 |
+
"alias": " - blimp_irregular_past_participle_adjectives"
|
157 |
+
},
|
158 |
+
"blimp_irregular_past_participle_verbs": {
|
159 |
+
"acc,none": 0.391,
|
160 |
+
"acc_stderr,none": 0.015438826294681775,
|
161 |
+
"alias": " - blimp_irregular_past_participle_verbs"
|
162 |
+
},
|
163 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
164 |
+
"acc,none": 0.528,
|
165 |
+
"acc_stderr,none": 0.015794475789511517,
|
166 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_1"
|
167 |
+
},
|
168 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
169 |
+
"acc,none": 0.536,
|
170 |
+
"acc_stderr,none": 0.015778243024904673,
|
171 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_2"
|
172 |
+
},
|
173 |
+
"blimp_left_branch_island_echo_question": {
|
174 |
+
"acc,none": 0.61,
|
175 |
+
"acc_stderr,none": 0.01543172505386673,
|
176 |
+
"alias": " - blimp_left_branch_island_echo_question"
|
177 |
+
},
|
178 |
+
"blimp_left_branch_island_simple_question": {
|
179 |
+
"acc,none": 0.516,
|
180 |
+
"acc_stderr,none": 0.015811198373114985,
|
181 |
+
"alias": " - blimp_left_branch_island_simple_question"
|
182 |
+
},
|
183 |
+
"blimp_matrix_question_npi_licensor_present": {
|
184 |
+
"acc,none": 0.596,
|
185 |
+
"acc_stderr,none": 0.015524980677122437,
|
186 |
+
"alias": " - blimp_matrix_question_npi_licensor_present"
|
187 |
+
},
|
188 |
+
"blimp_npi_present_1": {
|
189 |
+
"acc,none": 0.566,
|
190 |
+
"acc_stderr,none": 0.01568087656637504,
|
191 |
+
"alias": " - blimp_npi_present_1"
|
192 |
+
},
|
193 |
+
"blimp_npi_present_2": {
|
194 |
+
"acc,none": 0.586,
|
195 |
+
"acc_stderr,none": 0.01558354410417755,
|
196 |
+
"alias": " - blimp_npi_present_2"
|
197 |
+
},
|
198 |
+
"blimp_only_npi_licensor_present": {
|
199 |
+
"acc,none": 0.209,
|
200 |
+
"acc_stderr,none": 0.012864077288499339,
|
201 |
+
"alias": " - blimp_only_npi_licensor_present"
|
202 |
+
},
|
203 |
+
"blimp_only_npi_scope": {
|
204 |
+
"acc,none": 0.603,
|
205 |
+
"acc_stderr,none": 0.015480007449307982,
|
206 |
+
"alias": " - blimp_only_npi_scope"
|
207 |
+
},
|
208 |
+
"blimp_passive_1": {
|
209 |
+
"acc,none": 0.637,
|
210 |
+
"acc_stderr,none": 0.01521389044467136,
|
211 |
+
"alias": " - blimp_passive_1"
|
212 |
+
},
|
213 |
+
"blimp_passive_2": {
|
214 |
+
"acc,none": 0.652,
|
215 |
+
"acc_stderr,none": 0.015070604603768328,
|
216 |
+
"alias": " - blimp_passive_2"
|
217 |
+
},
|
218 |
+
"blimp_principle_A_c_command": {
|
219 |
+
"acc,none": 0.629,
|
220 |
+
"acc_stderr,none": 0.015283736211823096,
|
221 |
+
"alias": " - blimp_principle_A_c_command"
|
222 |
+
},
|
223 |
+
"blimp_principle_A_case_1": {
|
224 |
+
"acc,none": 0.31,
|
225 |
+
"acc_stderr,none": 0.014632638658632765,
|
226 |
+
"alias": " - blimp_principle_A_case_1"
|
227 |
+
},
|
228 |
+
"blimp_principle_A_case_2": {
|
229 |
+
"acc,none": 0.508,
|
230 |
+
"acc_stderr,none": 0.015817274929209084,
|
231 |
+
"alias": " - blimp_principle_A_case_2"
|
232 |
+
},
|
233 |
+
"blimp_principle_A_domain_1": {
|
234 |
+
"acc,none": 0.605,
|
235 |
+
"acc_stderr,none": 0.015466551464829328,
|
236 |
+
"alias": " - blimp_principle_A_domain_1"
|
237 |
+
},
|
238 |
+
"blimp_principle_A_domain_2": {
|
239 |
+
"acc,none": 0.517,
|
240 |
+
"acc_stderr,none": 0.015810153729833274,
|
241 |
+
"alias": " - blimp_principle_A_domain_2"
|
242 |
+
},
|
243 |
+
"blimp_principle_A_domain_3": {
|
244 |
+
"acc,none": 0.505,
|
245 |
+
"acc_stderr,none": 0.015818508944436743,
|
246 |
+
"alias": " - blimp_principle_A_domain_3"
|
247 |
+
},
|
248 |
+
"blimp_principle_A_reconstruction": {
|
249 |
+
"acc,none": 0.493,
|
250 |
+
"acc_stderr,none": 0.01581774956184353,
|
251 |
+
"alias": " - blimp_principle_A_reconstruction"
|
252 |
+
},
|
253 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
254 |
+
"acc,none": 0.442,
|
255 |
+
"acc_stderr,none": 0.015712507211864152,
|
256 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_1"
|
257 |
+
},
|
258 |
+
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259 |
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260 |
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|
261 |
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"alias": " - blimp_regular_plural_subject_verb_agreement_2"
|
262 |
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},
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263 |
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|
264 |
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|
265 |
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|
266 |
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"alias": " - blimp_sentential_negation_npi_licensor_present"
|
267 |
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},
|
268 |
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|
269 |
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|
270 |
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|
271 |
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272 |
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},
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273 |
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274 |
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|
275 |
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276 |
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277 |
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},
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278 |
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|
279 |
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|
280 |
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|
281 |
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"alias": " - blimp_superlative_quantifiers_1"
|
282 |
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},
|
283 |
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|
284 |
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"acc,none": 0.371,
|
285 |
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286 |
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287 |
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},
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288 |
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289 |
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|
290 |
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291 |
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"alias": " - blimp_tough_vs_raising_1"
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292 |
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},
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293 |
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294 |
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|
295 |
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296 |
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297 |
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298 |
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|
299 |
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|
300 |
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301 |
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302 |
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},
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303 |
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304 |
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305 |
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306 |
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307 |
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},
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308 |
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309 |
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310 |
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311 |
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"alias": " - blimp_wh_questions_object_gap"
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312 |
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},
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313 |
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314 |
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315 |
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316 |
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"alias": " - blimp_wh_questions_subject_gap"
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317 |
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},
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318 |
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319 |
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320 |
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321 |
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322 |
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},
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323 |
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324 |
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325 |
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326 |
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"alias": " - blimp_wh_vs_that_no_gap"
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327 |
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},
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328 |
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329 |
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330 |
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331 |
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332 |
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},
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333 |
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334 |
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335 |
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|
336 |
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337 |
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},
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338 |
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339 |
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340 |
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341 |
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342 |
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343 |
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344 |
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345 |
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346 |
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351 |
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352 |
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353 |
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"blimp_causative",
|
354 |
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355 |
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356 |
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|
357 |
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358 |
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|
359 |
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|
360 |
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|
361 |
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|
362 |
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|
363 |
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364 |
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365 |
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366 |
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367 |
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368 |
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369 |
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370 |
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371 |
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|
372 |
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|
373 |
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|
374 |
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|
375 |
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|
376 |
+
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|
377 |
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|
378 |
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|
379 |
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|
380 |
+
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|
381 |
+
"blimp_only_npi_scope",
|
382 |
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"blimp_tough_vs_raising_1",
|
383 |
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"blimp_transitive",
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384 |
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385 |
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386 |
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|
387 |
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|
388 |
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|
389 |
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|
390 |
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|
391 |
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392 |
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|
393 |
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394 |
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395 |
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396 |
+
"blimp_npi_present_2",
|
397 |
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"blimp_animate_subject_passive",
|
398 |
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|
399 |
+
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400 |
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401 |
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|
402 |
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|
403 |
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404 |
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405 |
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406 |
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407 |
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408 |
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409 |
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410 |
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|
411 |
+
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|
412 |
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"blimp_passive_2",
|
413 |
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|
414 |
+
"blimp_inchoative",
|
415 |
+
"blimp_animate_subject_trans",
|
416 |
+
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|
417 |
+
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|
418 |
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|
419 |
+
"blimp_matrix_question_npi_licensor_present"
|
420 |
+
]
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421 |
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},
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422 |
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423 |
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424 |
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425 |
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426 |
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427 |
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442 |
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445 |
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447 |
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}
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448 |
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449 |
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450 |
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451 |
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452 |
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453 |
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471 |
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473 |
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475 |
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476 |
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477 |
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478 |
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479 |
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480 |
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481 |
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492 |
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497 |
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499 |
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}
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500 |
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},
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501 |
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502 |
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"task": "blimp_animate_subject_passive",
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503 |
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504 |
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505 |
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506 |
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507 |
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518 |
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523 |
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524 |
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525 |
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}
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526 |
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},
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527 |
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528 |
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529 |
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530 |
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531 |
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532 |
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533 |
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}
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552 |
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553 |
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555 |
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556 |
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581 |
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582 |
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2365 |
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2369 |
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2399 |
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2400 |
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2401 |
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2403 |
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2404 |
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2405 |
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2407 |
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2408 |
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2409 |
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2411 |
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2413 |
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2417 |
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2421 |
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2423 |
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2424 |
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2425 |
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2428 |
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2429 |
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2433 |
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2437 |
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2441 |
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2445 |
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2448 |
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2449 |
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2451 |
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2452 |
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2453 |
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2455 |
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2456 |
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2457 |
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2459 |
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2460 |
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2461 |
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2464 |
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2465 |
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2466 |
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2467 |
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2468 |
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2469 |
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2470 |
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2471 |
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2472 |
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2473 |
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2476 |
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2477 |
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2480 |
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2481 |
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2484 |
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2485 |
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2487 |
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2488 |
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2489 |
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2492 |
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2493 |
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2495 |
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2496 |
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2497 |
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2498 |
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2499 |
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|
2500 |
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2501 |
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2502 |
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2503 |
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2504 |
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|
2505 |
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2506 |
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2507 |
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2508 |
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|
2509 |
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2510 |
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2511 |
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|
2512 |
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2513 |
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2514 |
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2515 |
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2516 |
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2517 |
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2518 |
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2519 |
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2520 |
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|
2521 |
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2522 |
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2523 |
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2524 |
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|
2525 |
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2526 |
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2527 |
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2528 |
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|
2529 |
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2530 |
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2531 |
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|
2532 |
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|
2533 |
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}
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pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-07-11T13-45-02.042989.json.bak
ADDED
@@ -0,0 +1,2606 @@
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"blimp": {
|
4 |
+
"acc,none": 0.5176865671641792,
|
5 |
+
"acc_stderr,none": 0.0018818593441844549,
|
6 |
+
"alias": "blimp"
|
7 |
+
},
|
8 |
+
"blimp_adjunct_island": {
|
9 |
+
"acc,none": 0.561,
|
10 |
+
"acc_stderr,none": 0.015701131345400736,
|
11 |
+
"alias": " - blimp_adjunct_island"
|
12 |
+
},
|
13 |
+
"blimp_anaphor_gender_agreement": {
|
14 |
+
"acc,none": 0.559,
|
15 |
+
"acc_stderr,none": 0.015708779894242766,
|
16 |
+
"alias": " - blimp_anaphor_gender_agreement"
|
17 |
+
},
|
18 |
+
"blimp_anaphor_number_agreement": {
|
19 |
+
"acc,none": 0.541,
|
20 |
+
"acc_stderr,none": 0.015766025737882314,
|
21 |
+
"alias": " - blimp_anaphor_number_agreement"
|
22 |
+
},
|
23 |
+
"blimp_animate_subject_passive": {
|
24 |
+
"acc,none": 0.593,
|
25 |
+
"acc_stderr,none": 0.015543249100255474,
|
26 |
+
"alias": " - blimp_animate_subject_passive"
|
27 |
+
},
|
28 |
+
"blimp_animate_subject_trans": {
|
29 |
+
"acc,none": 0.813,
|
30 |
+
"acc_stderr,none": 0.012336254828074168,
|
31 |
+
"alias": " - blimp_animate_subject_trans"
|
32 |
+
},
|
33 |
+
"blimp_causative": {
|
34 |
+
"acc,none": 0.456,
|
35 |
+
"acc_stderr,none": 0.01575792855397918,
|
36 |
+
"alias": " - blimp_causative"
|
37 |
+
},
|
38 |
+
"blimp_complex_NP_island": {
|
39 |
+
"acc,none": 0.509,
|
40 |
+
"acc_stderr,none": 0.0158167369950053,
|
41 |
+
"alias": " - blimp_complex_NP_island"
|
42 |
+
},
|
43 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
44 |
+
"acc,none": 0.547,
|
45 |
+
"acc_stderr,none": 0.015749255189977687,
|
46 |
+
"alias": " - blimp_coordinate_structure_constraint_complex_left_branch"
|
47 |
+
},
|
48 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
49 |
+
"acc,none": 0.531,
|
50 |
+
"acc_stderr,none": 0.015788865959538965,
|
51 |
+
"alias": " - blimp_coordinate_structure_constraint_object_extraction"
|
52 |
+
},
|
53 |
+
"blimp_determiner_noun_agreement_1": {
|
54 |
+
"acc,none": 0.539,
|
55 |
+
"acc_stderr,none": 0.015771104201283047,
|
56 |
+
"alias": " - blimp_determiner_noun_agreement_1"
|
57 |
+
},
|
58 |
+
"blimp_determiner_noun_agreement_2": {
|
59 |
+
"acc,none": 0.531,
|
60 |
+
"acc_stderr,none": 0.015788865959538965,
|
61 |
+
"alias": " - blimp_determiner_noun_agreement_2"
|
62 |
+
},
|
63 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
64 |
+
"acc,none": 0.498,
|
65 |
+
"acc_stderr,none": 0.01581917337430266,
|
66 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_1"
|
67 |
+
},
|
68 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
69 |
+
"acc,none": 0.474,
|
70 |
+
"acc_stderr,none": 0.015797897758042797,
|
71 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_2"
|
72 |
+
},
|
73 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
74 |
+
"acc,none": 0.54,
|
75 |
+
"acc_stderr,none": 0.0157685969143944,
|
76 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_2"
|
77 |
+
},
|
78 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
79 |
+
"acc,none": 0.541,
|
80 |
+
"acc_stderr,none": 0.015766025737882314,
|
81 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_1"
|
82 |
+
},
|
83 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
84 |
+
"acc,none": 0.522,
|
85 |
+
"acc_stderr,none": 0.01580397942816194,
|
86 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_2"
|
87 |
+
},
|
88 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
89 |
+
"acc,none": 0.499,
|
90 |
+
"acc_stderr,none": 0.015819268290576817,
|
91 |
+
"alias": " - blimp_determiner_noun_agreement_with_adjective_1"
|
92 |
+
},
|
93 |
+
"blimp_distractor_agreement_relational_noun": {
|
94 |
+
"acc,none": 0.478,
|
95 |
+
"acc_stderr,none": 0.01580397942816194,
|
96 |
+
"alias": " - blimp_distractor_agreement_relational_noun"
|
97 |
+
},
|
98 |
+
"blimp_distractor_agreement_relative_clause": {
|
99 |
+
"acc,none": 0.509,
|
100 |
+
"acc_stderr,none": 0.0158167369950053,
|
101 |
+
"alias": " - blimp_distractor_agreement_relative_clause"
|
102 |
+
},
|
103 |
+
"blimp_drop_argument": {
|
104 |
+
"acc,none": 0.663,
|
105 |
+
"acc_stderr,none": 0.01495508791865346,
|
106 |
+
"alias": " - blimp_drop_argument"
|
107 |
+
},
|
108 |
+
"blimp_ellipsis_n_bar_1": {
|
109 |
+
"acc,none": 0.431,
|
110 |
+
"acc_stderr,none": 0.01566794448817344,
|
111 |
+
"alias": " - blimp_ellipsis_n_bar_1"
|
112 |
+
},
|
113 |
+
"blimp_ellipsis_n_bar_2": {
|
114 |
+
"acc,none": 0.327,
|
115 |
+
"acc_stderr,none": 0.014842213153411162,
|
116 |
+
"alias": " - blimp_ellipsis_n_bar_2"
|
117 |
+
},
|
118 |
+
"blimp_existential_there_object_raising": {
|
119 |
+
"acc,none": 0.691,
|
120 |
+
"acc_stderr,none": 0.014619600977206347,
|
121 |
+
"alias": " - blimp_existential_there_object_raising"
|
122 |
+
},
|
123 |
+
"blimp_existential_there_quantifiers_1": {
|
124 |
+
"acc,none": 0.485,
|
125 |
+
"acc_stderr,none": 0.01581217964181488,
|
126 |
+
"alias": " - blimp_existential_there_quantifiers_1"
|
127 |
+
},
|
128 |
+
"blimp_existential_there_quantifiers_2": {
|
129 |
+
"acc,none": 0.833,
|
130 |
+
"acc_stderr,none": 0.011800434324644593,
|
131 |
+
"alias": " - blimp_existential_there_quantifiers_2"
|
132 |
+
},
|
133 |
+
"blimp_existential_there_subject_raising": {
|
134 |
+
"acc,none": 0.575,
|
135 |
+
"acc_stderr,none": 0.01564032031704017,
|
136 |
+
"alias": " - blimp_existential_there_subject_raising"
|
137 |
+
},
|
138 |
+
"blimp_expletive_it_object_raising": {
|
139 |
+
"acc,none": 0.59,
|
140 |
+
"acc_stderr,none": 0.015560917136921659,
|
141 |
+
"alias": " - blimp_expletive_it_object_raising"
|
142 |
+
},
|
143 |
+
"blimp_inchoative": {
|
144 |
+
"acc,none": 0.423,
|
145 |
+
"acc_stderr,none": 0.015630589090476255,
|
146 |
+
"alias": " - blimp_inchoative"
|
147 |
+
},
|
148 |
+
"blimp_intransitive": {
|
149 |
+
"acc,none": 0.555,
|
150 |
+
"acc_stderr,none": 0.015723301886761007,
|
151 |
+
"alias": " - blimp_intransitive"
|
152 |
+
},
|
153 |
+
"blimp_irregular_past_participle_adjectives": {
|
154 |
+
"acc,none": 0.531,
|
155 |
+
"acc_stderr,none": 0.015788865959538965,
|
156 |
+
"alias": " - blimp_irregular_past_participle_adjectives"
|
157 |
+
},
|
158 |
+
"blimp_irregular_past_participle_verbs": {
|
159 |
+
"acc,none": 0.391,
|
160 |
+
"acc_stderr,none": 0.015438826294681775,
|
161 |
+
"alias": " - blimp_irregular_past_participle_verbs"
|
162 |
+
},
|
163 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
164 |
+
"acc,none": 0.527,
|
165 |
+
"acc_stderr,none": 0.01579621855130273,
|
166 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_1"
|
167 |
+
},
|
168 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
169 |
+
"acc,none": 0.535,
|
170 |
+
"acc_stderr,none": 0.015780495050030086,
|
171 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_2"
|
172 |
+
},
|
173 |
+
"blimp_left_branch_island_echo_question": {
|
174 |
+
"acc,none": 0.609,
|
175 |
+
"acc_stderr,none": 0.015438826294681775,
|
176 |
+
"alias": " - blimp_left_branch_island_echo_question"
|
177 |
+
},
|
178 |
+
"blimp_left_branch_island_simple_question": {
|
179 |
+
"acc,none": 0.514,
|
180 |
+
"acc_stderr,none": 0.01581309754773093,
|
181 |
+
"alias": " - blimp_left_branch_island_simple_question"
|
182 |
+
},
|
183 |
+
"blimp_matrix_question_npi_licensor_present": {
|
184 |
+
"acc,none": 0.596,
|
185 |
+
"acc_stderr,none": 0.015524980677122437,
|
186 |
+
"alias": " - blimp_matrix_question_npi_licensor_present"
|
187 |
+
},
|
188 |
+
"blimp_npi_present_1": {
|
189 |
+
"acc,none": 0.567,
|
190 |
+
"acc_stderr,none": 0.01567663091218125,
|
191 |
+
"alias": " - blimp_npi_present_1"
|
192 |
+
},
|
193 |
+
"blimp_npi_present_2": {
|
194 |
+
"acc,none": 0.587,
|
195 |
+
"acc_stderr,none": 0.015577986829936457,
|
196 |
+
"alias": " - blimp_npi_present_2"
|
197 |
+
},
|
198 |
+
"blimp_only_npi_licensor_present": {
|
199 |
+
"acc,none": 0.209,
|
200 |
+
"acc_stderr,none": 0.012864077288499339,
|
201 |
+
"alias": " - blimp_only_npi_licensor_present"
|
202 |
+
},
|
203 |
+
"blimp_only_npi_scope": {
|
204 |
+
"acc,none": 0.601,
|
205 |
+
"acc_stderr,none": 0.015493193313163012,
|
206 |
+
"alias": " - blimp_only_npi_scope"
|
207 |
+
},
|
208 |
+
"blimp_passive_1": {
|
209 |
+
"acc,none": 0.637,
|
210 |
+
"acc_stderr,none": 0.01521389044467136,
|
211 |
+
"alias": " - blimp_passive_1"
|
212 |
+
},
|
213 |
+
"blimp_passive_2": {
|
214 |
+
"acc,none": 0.651,
|
215 |
+
"acc_stderr,none": 0.015080663991563052,
|
216 |
+
"alias": " - blimp_passive_2"
|
217 |
+
},
|
218 |
+
"blimp_principle_A_c_command": {
|
219 |
+
"acc,none": 0.629,
|
220 |
+
"acc_stderr,none": 0.015283736211823096,
|
221 |
+
"alias": " - blimp_principle_A_c_command"
|
222 |
+
},
|
223 |
+
"blimp_principle_A_case_1": {
|
224 |
+
"acc,none": 0.308,
|
225 |
+
"acc_stderr,none": 0.01460648312734278,
|
226 |
+
"alias": " - blimp_principle_A_case_1"
|
227 |
+
},
|
228 |
+
"blimp_principle_A_case_2": {
|
229 |
+
"acc,none": 0.509,
|
230 |
+
"acc_stderr,none": 0.0158167369950053,
|
231 |
+
"alias": " - blimp_principle_A_case_2"
|
232 |
+
},
|
233 |
+
"blimp_principle_A_domain_1": {
|
234 |
+
"acc,none": 0.605,
|
235 |
+
"acc_stderr,none": 0.015466551464829328,
|
236 |
+
"alias": " - blimp_principle_A_domain_1"
|
237 |
+
},
|
238 |
+
"blimp_principle_A_domain_2": {
|
239 |
+
"acc,none": 0.518,
|
240 |
+
"acc_stderr,none": 0.01580904569940659,
|
241 |
+
"alias": " - blimp_principle_A_domain_2"
|
242 |
+
},
|
243 |
+
"blimp_principle_A_domain_3": {
|
244 |
+
"acc,none": 0.504,
|
245 |
+
"acc_stderr,none": 0.01581879370351084,
|
246 |
+
"alias": " - blimp_principle_A_domain_3"
|
247 |
+
},
|
248 |
+
"blimp_principle_A_reconstruction": {
|
249 |
+
"acc,none": 0.494,
|
250 |
+
"acc_stderr,none": 0.015818160898606836,
|
251 |
+
"alias": " - blimp_principle_A_reconstruction"
|
252 |
+
},
|
253 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
254 |
+
"acc,none": 0.441,
|
255 |
+
"acc_stderr,none": 0.015708779894242766,
|
256 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_1"
|
257 |
+
},
|
258 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
259 |
+
"acc,none": 0.515,
|
260 |
+
"acc_stderr,none": 0.01581217964181488,
|
261 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_2"
|
262 |
+
},
|
263 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
264 |
+
"acc,none": 0.657,
|
265 |
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2257 |
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2284 |
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2285 |
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2299 |
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2301 |
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2302 |
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2307 |
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2315 |
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2327 |
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2329 |
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2331 |
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2333 |
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2335 |
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2337 |
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2339 |
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2340 |
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2341 |
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2342 |
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2343 |
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2344 |
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2345 |
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2346 |
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2347 |
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2349 |
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2351 |
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2353 |
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2355 |
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2357 |
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2359 |
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2360 |
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2361 |
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2362 |
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2363 |
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2364 |
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2365 |
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2367 |
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2368 |
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2369 |
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2370 |
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2371 |
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2372 |
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2373 |
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2374 |
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2375 |
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2376 |
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2377 |
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2379 |
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2380 |
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2381 |
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2382 |
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2383 |
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2384 |
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2385 |
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2387 |
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2388 |
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2389 |
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2390 |
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2391 |
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2392 |
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2393 |
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2396 |
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2397 |
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2399 |
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2400 |
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2401 |
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2402 |
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2403 |
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2404 |
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2405 |
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2406 |
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2407 |
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2408 |
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2409 |
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2410 |
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2411 |
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2412 |
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2413 |
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2414 |
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2415 |
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2416 |
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2417 |
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2418 |
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2419 |
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2420 |
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2421 |
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2422 |
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2423 |
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2424 |
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2425 |
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2426 |
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2427 |
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2428 |
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2429 |
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2430 |
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2431 |
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2432 |
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2433 |
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2434 |
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2435 |
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2436 |
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2437 |
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2438 |
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2439 |
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2440 |
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2441 |
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2442 |
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2444 |
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2445 |
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2447 |
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2448 |
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2449 |
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2450 |
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2451 |
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|
2452 |
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|
2453 |
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|
2454 |
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2455 |
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|
2456 |
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|
2457 |
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2458 |
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2459 |
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|
2460 |
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2461 |
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2462 |
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2463 |
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|
2464 |
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2465 |
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2466 |
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2467 |
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|
2468 |
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|
2469 |
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2470 |
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2471 |
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2472 |
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|
2473 |
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2474 |
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2475 |
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2476 |
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|
2477 |
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2478 |
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2479 |
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2480 |
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2481 |
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2482 |
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2483 |
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2484 |
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2485 |
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2486 |
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2487 |
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2488 |
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|
2489 |
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2490 |
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2491 |
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2492 |
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|
2493 |
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2494 |
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2495 |
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|
2496 |
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|
2497 |
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2498 |
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2499 |
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|
2500 |
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|
2501 |
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|
2502 |
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2503 |
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|
2504 |
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|
2505 |
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2506 |
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2507 |
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|
2508 |
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|
2509 |
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|
2510 |
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|
2511 |
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|
2512 |
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|
2513 |
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},
|
2514 |
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|
2515 |
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|
2516 |
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|
2517 |
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2518 |
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2519 |
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|
2520 |
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|
2521 |
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2522 |
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|
2523 |
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|
2524 |
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|
2525 |
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|
2526 |
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|
2527 |
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|
2528 |
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|
2529 |
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|
2530 |
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2531 |
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|
2532 |
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|
2533 |
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2534 |
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|
2535 |
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|
2536 |
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|
2537 |
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|
2538 |
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|
2539 |
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|
2540 |
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|
2541 |
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pythia-14m-seed1/step0/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-34-26.878314.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
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2 |
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"results": {
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"lambada_openai": {
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}
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57 |
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},
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58 |
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"group_subtasks": {
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59 |
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"hendrycks_math": [
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|
61 |
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|
62 |
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"hendrycks_math_num_theory",
|
63 |
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|
64 |
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|
65 |
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67 |
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],
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"lambada_openai": []
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70 |
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"configs": {
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71 |
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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77 |
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{
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}
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}
|
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"transformers_version": "4.40.2",
|
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|
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"task_hashes": {},
|
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"model_source": "hf",
|
477 |
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"model_name": "EleutherAI/pythia-14m-seed1",
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 586431.450237646,
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|
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}
|
pythia-14m-seed1/step1/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-38-36.798754.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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3 |
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"lambada_openai": {
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"alias": " - hendrycks_math_algebra"
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},
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},
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"hendrycks_math_geometry": {
|
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_geometry"
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},
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"hendrycks_math_intermediate_algebra": {
|
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"exact_match,none": 0.0,
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|
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"alias": " - hendrycks_math_intermediate_algebra"
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
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},
|
45 |
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"hendrycks_math_precalc": {
|
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|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
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}
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},
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"groups": {
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"hendrycks_math": {
|
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|
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55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
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"until": [
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101 |
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"Problem:"
|
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],
|
103 |
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|
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"temperature": 0.0
|
105 |
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},
|
106 |
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
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"dataset_name": "counting_and_probability",
|
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|
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|
121 |
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},
|
122 |
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|
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|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
131 |
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step1000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-20-43.708459.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 165785.28222773902,
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5 |
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"perplexity_stderr,none": 10148.605380657666,
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"acc,none": 0.0,
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"alias": "lambada_openai"
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},
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"exact_match_stderr,none": 0.0,
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
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34 |
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},
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35 |
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"hendrycks_math_num_theory": {
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36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
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"hendrycks_math_prealgebra": {
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41 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_prealgebra"
|
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},
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45 |
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"hendrycks_math_precalc": {
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46 |
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"exact_match,none": 0.0,
|
47 |
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
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100 |
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"until": [
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101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
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130 |
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
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133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
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136 |
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"higher_is_better": true
|
137 |
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}
|
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],
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139 |
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"output_type": "generate_until",
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140 |
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"generation_kwargs": {
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141 |
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"until": [
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142 |
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"Problem:"
|
143 |
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],
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144 |
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"do_sample": false,
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145 |
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"temperature": 0.0
|
146 |
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},
|
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
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172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
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"output_type": "generate_until",
|
181 |
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"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
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"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
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"task": "hendrycks_math_intermediate_algebra",
|
196 |
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"group": [
|
197 |
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"math_word_problems"
|
198 |
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],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
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"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
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},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
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"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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"num_fewshot": 0,
|
214 |
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|
pythia-14m-seed1/step10000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-58-13.399858.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
|
2 |
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"results": {
|
3 |
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|
4 |
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5 |
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6 |
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9 |
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},
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10 |
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11 |
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12 |
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|
13 |
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"alias": "hendrycks_math"
|
14 |
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},
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15 |
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|
16 |
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17 |
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18 |
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19 |
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},
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20 |
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|
21 |
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22 |
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24 |
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},
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25 |
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|
26 |
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|
27 |
+
"exact_match_stderr,none": 0.0,
|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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|
31 |
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|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
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39 |
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},
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40 |
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42 |
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43 |
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44 |
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49 |
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}
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50 |
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},
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|
56 |
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}
|
57 |
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},
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58 |
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"group_subtasks": {
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|
60 |
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|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
+
"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
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"hendrycks_math_algebra"
|
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],
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"lambada_openai": []
|
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},
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70 |
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"task": "hendrycks_math_algebra",
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73 |
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"group": [
|
74 |
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|
75 |
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],
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76 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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{
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],
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105 |
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},
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}
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111 |
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},
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112 |
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113 |
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"task": "hendrycks_math_counting_and_prob",
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114 |
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"group": [
|
115 |
+
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|
116 |
+
],
|
117 |
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|
118 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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132 |
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133 |
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{
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140 |
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141 |
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"Problem:"
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],
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|
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},
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|
148 |
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|
149 |
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"metadata": {
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|
151 |
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}
|
152 |
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},
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153 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
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156 |
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"math_word_problems"
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157 |
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],
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158 |
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160 |
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|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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|
173 |
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|
174 |
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{
|
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"metric": "exact_match",
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177 |
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"higher_is_better": true
|
178 |
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}
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],
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180 |
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"output_type": "generate_until",
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181 |
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182 |
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184 |
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],
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|
186 |
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|
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},
|
188 |
+
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|
189 |
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"should_decontaminate": false,
|
190 |
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"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
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"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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|
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|
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{
|
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"metric": "exact_match",
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"aggregation": "mean",
|
218 |
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"higher_is_better": true
|
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}
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],
|
221 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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|
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],
|
226 |
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|
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|
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},
|
229 |
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|
230 |
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"should_decontaminate": false,
|
231 |
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"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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|
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|
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
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"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
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],
|
267 |
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|
268 |
+
"temperature": 0.0
|
269 |
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},
|
270 |
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|
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|
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|
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}
|
pythia-14m-seed1/step11000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-02-19.860338.json
ADDED
@@ -0,0 +1,482 @@
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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|
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}
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},
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|
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"trust_remote_code": true
|
326 |
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},
|
327 |
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"training_split": "train",
|
328 |
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"test_split": "test",
|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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"description": "",
|
334 |
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"target_delimiter": " ",
|
335 |
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"fewshot_delimiter": "\n\n",
|
336 |
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337 |
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|
338 |
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{
|
339 |
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|
340 |
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|
341 |
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"higher_is_better": true
|
342 |
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|
343 |
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|
344 |
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"output_type": "generate_until",
|
345 |
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|
346 |
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"until": [
|
347 |
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|
348 |
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|
349 |
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|
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|
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|
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|
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|
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"metadata": {
|
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|
356 |
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}
|
357 |
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},
|
358 |
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"lambada_openai": {
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359 |
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"task": "lambada_openai",
|
360 |
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|
361 |
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|
362 |
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],
|
363 |
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364 |
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365 |
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|
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"trust_remote_code": true
|
367 |
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368 |
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"test_split": "test",
|
369 |
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"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
370 |
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"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
371 |
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"description": "",
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|
380 |
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|
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|
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|
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|
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|
390 |
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|
391 |
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|
392 |
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|
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|
394 |
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|
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|
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},
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"git_hash": "51a7ca9",
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"date": 1723471114.3282776,
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|
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|
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477 |
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|
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|
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}
|
pythia-14m-seed1/step12000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-06-26.034288.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
+
{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1198.372281061769,
|
5 |
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"acc,none": 0.11236173103046769,
|
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"alias": "lambada_openai"
|
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|
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|
12 |
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|
13 |
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|
14 |
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|
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|
16 |
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|
17 |
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|
18 |
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"alias": " - hendrycks_math_algebra"
|
19 |
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|
20 |
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|
21 |
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|
22 |
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|
23 |
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"alias": " - hendrycks_math_counting_and_prob"
|
24 |
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},
|
25 |
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|
26 |
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|
27 |
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"exact_match_stderr,none": 0.0,
|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
38 |
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"hendrycks_math_geometry",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"task": "hendrycks_math_precalc",
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319 |
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|
320 |
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"math_word_problems"
|
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],
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pythia-14m-seed1/step128/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-08-24.350787.json
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@@ -0,0 +1,482 @@
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{
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"group_subtasks": {
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"lambada_openai": []
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70 |
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"configs": {
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71 |
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72 |
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74 |
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pythia-14m-seed1/step13000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-10-32.725397.json
ADDED
@@ -0,0 +1,482 @@
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{
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{
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"transformers_version": "4.40.2",
|
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"upper_git_hash": null,
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"task_hashes": {},
|
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"model_source": "hf",
|
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"model_name": "EleutherAI/pythia-14m-seed1",
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478 |
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 592204.226383305,
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}
|
pythia-14m-seed1/step14000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-14-36.870500.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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3 |
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"lambada_openai": {
|
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"perplexity,none": 1330.1246672723364,
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"alias": " - hendrycks_math_algebra"
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},
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"alias": " - hendrycks_math_counting_and_prob"
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},
|
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"hendrycks_math_geometry": {
|
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_geometry"
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},
|
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
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},
|
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"hendrycks_math_num_theory": {
|
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
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"alias": " - hendrycks_math_precalc"
|
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}
|
50 |
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},
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51 |
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"groups": {
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"hendrycks_math": {
|
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"alias": "hendrycks_math"
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56 |
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}
|
57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
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"metric_list": [
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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"until": [
|
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"Problem:"
|
102 |
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],
|
103 |
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|
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"temperature": 0.0
|
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},
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
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"dataset_name": "counting_and_probability",
|
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|
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|
121 |
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},
|
122 |
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"training_split": "train",
|
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step15000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-18-44.185809.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1300.454681174635,
|
5 |
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"perplexity_stderr,none": 59.02578571631813,
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"acc,none": 0.09023869590529789,
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"acc_stderr,none": 0.003991831568038306,
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"alias": "lambada_openai"
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},
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": "hendrycks_math"
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},
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15 |
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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21 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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24 |
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},
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25 |
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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27 |
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"exact_match_stderr,none": 0.0,
|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
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43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
47 |
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
|
53 |
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
90 |
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
106 |
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
+
],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
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131 |
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
+
"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
|
172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
+
"output_type": "generate_until",
|
181 |
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"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
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},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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"num_fewshot": 0,
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|
pythia-14m-seed1/step16/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-55-59.794548.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
|
2 |
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|
3 |
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|
4 |
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5 |
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7 |
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8 |
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|
9 |
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},
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10 |
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|
11 |
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12 |
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13 |
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14 |
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},
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15 |
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|
16 |
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17 |
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18 |
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19 |
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},
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20 |
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|
21 |
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22 |
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23 |
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"alias": " - hendrycks_math_counting_and_prob"
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24 |
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},
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25 |
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|
26 |
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|
27 |
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|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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|
31 |
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|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
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35 |
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|
36 |
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|
37 |
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|
38 |
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"alias": " - hendrycks_math_num_theory"
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39 |
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40 |
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42 |
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43 |
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44 |
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},
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45 |
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48 |
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49 |
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}
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50 |
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},
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55 |
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|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
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|
60 |
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|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
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67 |
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],
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"lambada_openai": []
|
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},
|
70 |
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71 |
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|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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|
74 |
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|
75 |
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],
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76 |
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|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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{
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],
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105 |
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},
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}
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111 |
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},
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112 |
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113 |
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114 |
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|
115 |
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|
116 |
+
],
|
117 |
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|
118 |
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124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
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"description": "",
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129 |
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132 |
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133 |
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{
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140 |
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141 |
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"Problem:"
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143 |
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],
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|
145 |
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|
146 |
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},
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147 |
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|
148 |
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|
149 |
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"metadata": {
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150 |
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|
151 |
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}
|
152 |
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},
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153 |
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154 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
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156 |
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"math_word_problems"
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157 |
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],
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158 |
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159 |
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160 |
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|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
|
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|
173 |
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|
174 |
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{
|
175 |
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"metric": "exact_match",
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176 |
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177 |
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"higher_is_better": true
|
178 |
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}
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179 |
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],
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180 |
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"output_type": "generate_until",
|
181 |
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182 |
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183 |
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184 |
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],
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185 |
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|
186 |
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|
187 |
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},
|
188 |
+
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|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
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|
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214 |
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|
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{
|
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"metric": "exact_match",
|
217 |
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"aggregation": "mean",
|
218 |
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"higher_is_better": true
|
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}
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],
|
221 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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|
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"Problem:"
|
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],
|
226 |
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|
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|
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},
|
229 |
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|
230 |
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"should_decontaminate": false,
|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
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"description": "",
|
252 |
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"target_delimiter": " ",
|
253 |
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"fewshot_delimiter": "\n\n",
|
254 |
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|
255 |
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|
256 |
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{
|
257 |
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"metric": "exact_match",
|
258 |
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
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"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
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],
|
267 |
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"do_sample": false,
|
268 |
+
"temperature": 0.0
|
269 |
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},
|
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|
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|
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|
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}
|
pythia-14m-seed1/step16000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-22-52.656419.json
ADDED
@@ -0,0 +1,482 @@
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{
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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|
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}
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},
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|
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"task": "hendrycks_math_num_theory",
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],
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
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"doc_to_target": "{{answer}}",
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325 |
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"trust_remote_code": true
|
326 |
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|
327 |
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|
328 |
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|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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|
334 |
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"target_delimiter": " ",
|
335 |
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336 |
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337 |
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|
338 |
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{
|
339 |
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|
340 |
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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|
345 |
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|
346 |
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|
347 |
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|
348 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
361 |
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|
362 |
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],
|
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364 |
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365 |
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|
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|
367 |
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|
368 |
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|
369 |
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"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
370 |
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"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
371 |
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|
380 |
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|
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|
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|
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|
390 |
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391 |
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|
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|
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|
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|
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},
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"git_hash": "51a7ca9",
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"date": 1723472347.0784972,
|
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|
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}
|
pythia-14m-seed1/step17000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-27-02.544847.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"lambada_openai": {
|
4 |
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"perplexity,none": 1362.146262073091,
|
5 |
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"acc,none": 0.09800116437026975,
|
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|
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|
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|
14 |
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|
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|
16 |
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|
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|
18 |
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"alias": " - hendrycks_math_algebra"
|
19 |
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|
20 |
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|
21 |
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|
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|
23 |
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|
24 |
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|
25 |
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|
26 |
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|
27 |
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"exact_match_stderr,none": 0.0,
|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
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|
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|
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|
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|
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|
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|
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],
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pythia-14m-seed1/step18000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-31-07.685214.json
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@@ -0,0 +1,482 @@
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1 |
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{
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"group_subtasks": {
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"hendrycks_math": [
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65 |
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"lambada_openai": []
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},
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70 |
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"configs": {
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71 |
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"hendrycks_math_algebra": {
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72 |
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"task": "hendrycks_math_algebra",
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"group": [
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],
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"description": "",
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pythia-14m-seed1/step19000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-35-11.859545.json
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@@ -0,0 +1,482 @@
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{
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{
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"transformers_version": "4.40.2",
|
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"upper_git_hash": null,
|
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"task_hashes": {},
|
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"model_source": "hf",
|
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"model_name": "EleutherAI/pythia-14m-seed1",
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478 |
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 593685.951817292,
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}
|
pythia-14m-seed1/step2/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-42-57.791663.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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"lambada_openai": {
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},
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"hendrycks_math_geometry": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_geometry"
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},
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"hendrycks_math_intermediate_algebra": {
|
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|
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36 |
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"exact_match_stderr,none": 0.0,
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38 |
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"alias": " - hendrycks_math_num_theory"
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},
|
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"hendrycks_math_prealgebra": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
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},
|
45 |
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"hendrycks_math_precalc": {
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|
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"alias": " - hendrycks_math_precalc"
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}
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},
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"groups": {
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"hendrycks_math": {
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|
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"alias": "hendrycks_math"
|
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}
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57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
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"group": [
|
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"math_word_problems"
|
75 |
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],
|
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
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"metric_list": [
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
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],
|
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"output_type": "generate_until",
|
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"generation_kwargs": {
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"until": [
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"Problem:"
|
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],
|
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|
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"temperature": 0.0
|
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},
|
106 |
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
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"version": 1.0
|
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}
|
111 |
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},
|
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"hendrycks_math_counting_and_prob": {
|
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"task": "hendrycks_math_counting_and_prob",
|
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"group": [
|
115 |
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"math_word_problems"
|
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
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|
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|
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|
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},
|
122 |
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|
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
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{
|
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step2000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-25-00.074484.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 6442.748239816954,
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5 |
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"perplexity_stderr,none": 263.150168034604,
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"acc,none": 0.02736270133902581,
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"acc_stderr,none": 0.0022728303384483696,
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"alias": "lambada_openai"
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},
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"hendrycks_math": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
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43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
47 |
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
|
52 |
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"hendrycks_math": {
|
53 |
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
+
"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
+
"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
106 |
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
+
],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
+
},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
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131 |
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
+
"training_split": "train",
|
164 |
+
"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
+
"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
|
172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
+
"output_type": "generate_until",
|
181 |
+
"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
+
},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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pythia-14m-seed1/step20000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-39-21.895446.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
|
2 |
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"results": {
|
3 |
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|
4 |
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5 |
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6 |
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|
9 |
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},
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10 |
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|
11 |
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12 |
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|
13 |
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"alias": "hendrycks_math"
|
14 |
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},
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15 |
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|
16 |
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17 |
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18 |
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19 |
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},
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20 |
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|
21 |
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22 |
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23 |
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"alias": " - hendrycks_math_counting_and_prob"
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24 |
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},
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25 |
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|
26 |
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"exact_match,none": 0.0,
|
27 |
+
"exact_match_stderr,none": 0.0,
|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
+
},
|
30 |
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|
31 |
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"exact_match,none": 0.0,
|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
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39 |
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},
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40 |
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42 |
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43 |
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44 |
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},
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48 |
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|
49 |
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}
|
50 |
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},
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51 |
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"groups": {
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55 |
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|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
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"hendrycks_math": [
|
60 |
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|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
+
"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
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70 |
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71 |
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|
72 |
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"task": "hendrycks_math_algebra",
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73 |
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"group": [
|
74 |
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|
75 |
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],
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76 |
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},
|
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|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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{
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|
102 |
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],
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105 |
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},
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}
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111 |
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},
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113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
+
"group": [
|
115 |
+
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|
116 |
+
],
|
117 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
+
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121 |
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|
122 |
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124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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{
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140 |
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141 |
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],
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|
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"temperature": 0.0
|
146 |
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},
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|
148 |
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|
149 |
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"metadata": {
|
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"version": 1.0
|
151 |
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}
|
152 |
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},
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153 |
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154 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
+
],
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158 |
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|
159 |
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|
160 |
+
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|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
+
"training_split": "train",
|
164 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
|
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|
173 |
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|
174 |
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{
|
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"metric": "exact_match",
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176 |
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|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
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"output_type": "generate_until",
|
181 |
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|
182 |
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184 |
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],
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185 |
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|
186 |
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|
187 |
+
},
|
188 |
+
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|
189 |
+
"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
+
},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
+
"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
+
"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
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|
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|
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|
214 |
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|
215 |
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
218 |
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"higher_is_better": true
|
219 |
+
}
|
220 |
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],
|
221 |
+
"output_type": "generate_until",
|
222 |
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"generation_kwargs": {
|
223 |
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|
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"Problem:"
|
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],
|
226 |
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|
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|
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},
|
229 |
+
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|
230 |
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"should_decontaminate": false,
|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
+
"description": "",
|
252 |
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"target_delimiter": " ",
|
253 |
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"fewshot_delimiter": "\n\n",
|
254 |
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|
255 |
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"metric_list": [
|
256 |
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{
|
257 |
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"metric": "exact_match",
|
258 |
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
+
"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
+
],
|
267 |
+
"do_sample": false,
|
268 |
+
"temperature": 0.0
|
269 |
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|
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|
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|
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|
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|
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}
|
pythia-14m-seed1/step21000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-43-30.344693.json
ADDED
@@ -0,0 +1,482 @@
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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}
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},
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"task": "hendrycks_math_num_theory",
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],
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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|
324 |
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|
325 |
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|
326 |
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|
327 |
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|
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|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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|
334 |
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|
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{
|
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|
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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|
345 |
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|
346 |
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|
347 |
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|
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|
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|
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361 |
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|
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|
367 |
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"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
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371 |
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"git_hash": "51a7ca9",
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"date": 1723473583.7465441,
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}
|
pythia-14m-seed1/step22000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-47-38.819675.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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|
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|
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|
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|
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|
18 |
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|
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|
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|
21 |
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|
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|
24 |
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|
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|
26 |
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|
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|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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|
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|
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|
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|
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pythia-14m-seed1/step23000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-51-49.064750.json
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1 |
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{
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
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60 |
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|
61 |
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|
62 |
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63 |
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|
64 |
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|
65 |
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|
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"lambada_openai": []
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
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"task": "hendrycks_math_algebra",
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73 |
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"group": [
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74 |
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],
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76 |
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77 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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pythia-14m-seed1/step24000/EleutherAI__pythia-14m-seed1/results_2024-08-12T07-55-57.280297.json
ADDED
@@ -0,0 +1,482 @@
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{
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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{
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"metadata": {
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}
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"task": "hendrycks_math_counting_and_prob",
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"transformers_version": "4.40.2",
|
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"upper_git_hash": null,
|
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"task_hashes": {},
|
476 |
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"model_source": "hf",
|
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"model_name": "EleutherAI/pythia-14m-seed1",
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478 |
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 594929.263761255,
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"end_time": 595169.48710706,
|
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"total_evaluation_time_seconds": "240.22334580507595"
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}
|
pythia-14m-seed1/step25000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-00-23.273296.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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3 |
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"lambada_openai": {
|
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"alias": "hendrycks_math"
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
|
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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28 |
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"alias": " - hendrycks_math_geometry"
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},
|
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
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},
|
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
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}
|
50 |
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},
|
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"groups": {
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52 |
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"hendrycks_math": {
|
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
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{
|
93 |
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
106 |
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
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|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
131 |
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
+
}
|
152 |
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},
|
153 |
+
"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
+
"dataset_name": "geometry",
|
160 |
+
"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step256/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-12-30.518046.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1557200.1507235158,
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5 |
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"perplexity_stderr,none": 120989.57362868116,
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"acc,none": 0.0,
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"acc_stderr,none": 0.0,
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"alias": "lambada_openai"
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},
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"hendrycks_math": {
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11 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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28 |
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"alias": " - hendrycks_math_geometry"
|
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},
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30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
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35 |
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
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"hendrycks_math_prealgebra": {
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41 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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43 |
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"alias": " - hendrycks_math_prealgebra"
|
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
47 |
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
|
53 |
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
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100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
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130 |
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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132 |
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"metric_list": [
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133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
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136 |
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"higher_is_better": true
|
137 |
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}
|
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],
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139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
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141 |
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"until": [
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142 |
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"Problem:"
|
143 |
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],
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144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
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172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
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"output_type": "generate_until",
|
181 |
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"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
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],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
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},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
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"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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"num_fewshot": 0,
|
214 |
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pythia-14m-seed1/step26000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-04-43.508436.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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|
4 |
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5 |
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6 |
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8 |
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|
9 |
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},
|
10 |
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|
11 |
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|
12 |
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|
13 |
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"alias": "hendrycks_math"
|
14 |
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},
|
15 |
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|
16 |
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17 |
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18 |
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"alias": " - hendrycks_math_algebra"
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19 |
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},
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20 |
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|
21 |
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22 |
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23 |
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"alias": " - hendrycks_math_counting_and_prob"
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24 |
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},
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25 |
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|
26 |
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"exact_match,none": 0.0,
|
27 |
+
"exact_match_stderr,none": 0.0,
|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
+
},
|
30 |
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|
31 |
+
"exact_match,none": 0.0,
|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
+
"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
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41 |
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42 |
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43 |
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"alias": " - hendrycks_math_prealgebra"
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44 |
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},
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45 |
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48 |
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|
49 |
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}
|
50 |
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},
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51 |
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"groups": {
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55 |
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|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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|
60 |
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|
61 |
+
"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
+
"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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+
},
|
70 |
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71 |
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|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
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76 |
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77 |
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},
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83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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{
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|
102 |
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],
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105 |
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},
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}
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111 |
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},
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112 |
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113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
+
"group": [
|
115 |
+
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|
116 |
+
],
|
117 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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121 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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132 |
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133 |
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{
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],
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140 |
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141 |
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"until": [
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"Problem:"
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],
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|
145 |
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},
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|
148 |
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|
149 |
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"metadata": {
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|
151 |
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}
|
152 |
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},
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153 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
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156 |
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"math_word_problems"
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157 |
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],
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158 |
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|
159 |
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160 |
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|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
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|
173 |
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|
174 |
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{
|
175 |
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"metric": "exact_match",
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|
177 |
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"higher_is_better": true
|
178 |
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}
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179 |
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],
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180 |
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"output_type": "generate_until",
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181 |
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182 |
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184 |
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],
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|
186 |
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|
187 |
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},
|
188 |
+
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|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
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"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
|
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|
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|
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{
|
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"metric": "exact_match",
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"aggregation": "mean",
|
218 |
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"higher_is_better": true
|
219 |
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}
|
220 |
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],
|
221 |
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"output_type": "generate_until",
|
222 |
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"generation_kwargs": {
|
223 |
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|
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|
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],
|
226 |
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|
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|
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},
|
229 |
+
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|
230 |
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"should_decontaminate": false,
|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
+
"description": "",
|
252 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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|
255 |
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"metric_list": [
|
256 |
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{
|
257 |
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"metric": "exact_match",
|
258 |
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
+
"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
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],
|
267 |
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"do_sample": false,
|
268 |
+
"temperature": 0.0
|
269 |
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},
|
270 |
+
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|
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|
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|
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}
|
pythia-14m-seed1/step27000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-08-54.479591.json
ADDED
@@ -0,0 +1,482 @@
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{
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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}
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},
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|
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"task": "hendrycks_math_num_theory",
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],
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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325 |
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"trust_remote_code": true
|
326 |
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|
327 |
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|
328 |
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|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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|
334 |
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|
335 |
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336 |
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|
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{
|
339 |
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|
340 |
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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|
345 |
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|
346 |
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|
347 |
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|
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|
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"metadata": {
|
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|
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|
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|
361 |
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|
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],
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364 |
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|
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|
367 |
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|
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369 |
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|
370 |
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|
371 |
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|
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|
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|
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391 |
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|
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|
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|
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"date": 1723475105.3772542,
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}
|
pythia-14m-seed1/step28000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-13-03.437554.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
+
{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1183.8921931457348,
|
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|
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|
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|
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|
16 |
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|
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|
18 |
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"alias": " - hendrycks_math_algebra"
|
19 |
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|
20 |
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|
21 |
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|
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|
23 |
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|
24 |
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|
25 |
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|
26 |
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|
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|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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|
33 |
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|
34 |
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|
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|
36 |
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|
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|
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|
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|
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|
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|
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|
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|
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],
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pythia-14m-seed1/step29000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-17-10.235976.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
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46 |
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50 |
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"alias": "hendrycks_math"
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}
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57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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|
61 |
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|
62 |
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|
63 |
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|
64 |
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|
65 |
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|
66 |
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|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
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74 |
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|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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81 |
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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85 |
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"doc_to_target": "{{answer}}",
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86 |
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pythia-14m-seed1/step3000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-29-07.176670.json
ADDED
@@ -0,0 +1,482 @@
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{
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"group_subtasks": {
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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{
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"output_type": "generate_until",
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"metadata": {
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}
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"task": "hendrycks_math_counting_and_prob",
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"transformers_version": "4.40.2",
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"task_hashes": {},
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"model_source": "hf",
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"model_name": "EleutherAI/pythia-14m-seed1",
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 589720.380247604,
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}
|
pythia-14m-seed1/step30000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-21-21.161950.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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"lambada_openai": {
|
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"alias": " - hendrycks_math_algebra"
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},
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},
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"hendrycks_math_geometry": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_geometry"
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},
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
|
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"alias": " - hendrycks_math_intermediate_algebra"
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"hendrycks_math_num_theory": {
|
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
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},
|
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_precalc"
|
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}
|
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},
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"groups": {
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"hendrycks_math": {
|
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"alias": "hendrycks_math"
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}
|
57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
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"dataset_kwargs": {
|
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
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"until": [
|
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"Problem:"
|
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],
|
103 |
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|
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"temperature": 0.0
|
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},
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
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|
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|
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|
121 |
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},
|
122 |
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"training_split": "train",
|
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step31000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-25-31.758593.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 942.471093909997,
|
5 |
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"perplexity_stderr,none": 44.99705295268038,
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"acc,none": 0.1280807296720357,
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"acc_stderr,none": 0.004655776323580886,
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"alias": "lambada_openai"
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},
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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21 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
|
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
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28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
|
53 |
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
90 |
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
+
],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
132 |
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"metric_list": [
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133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
|
172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
+
"output_type": "generate_until",
|
181 |
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"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
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},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
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"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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"num_fewshot": 0,
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pythia-14m-seed1/step32/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-00-10.088125.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
|
2 |
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|
3 |
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|
4 |
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5 |
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|
9 |
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},
|
10 |
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|
11 |
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12 |
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|
13 |
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|
14 |
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},
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15 |
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|
16 |
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17 |
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18 |
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19 |
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},
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20 |
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|
21 |
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22 |
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23 |
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24 |
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},
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25 |
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|
26 |
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|
27 |
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|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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|
31 |
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|
32 |
+
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|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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|
36 |
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|
37 |
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|
38 |
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"alias": " - hendrycks_math_num_theory"
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39 |
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40 |
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42 |
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43 |
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44 |
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},
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45 |
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48 |
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49 |
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}
|
50 |
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},
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55 |
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|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
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|
60 |
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|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
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|
67 |
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],
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"lambada_openai": []
|
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},
|
70 |
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71 |
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|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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|
74 |
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|
75 |
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],
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76 |
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83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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{
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],
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105 |
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},
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}
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111 |
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112 |
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113 |
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114 |
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|
115 |
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|
116 |
+
],
|
117 |
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|
118 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
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"description": "",
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"target_delimiter": " ",
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133 |
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{
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140 |
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141 |
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],
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145 |
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},
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|
148 |
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|
149 |
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"metadata": {
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|
151 |
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}
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152 |
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},
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153 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
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156 |
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"math_word_problems"
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157 |
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],
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158 |
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160 |
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|
161 |
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|
162 |
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},
|
163 |
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|
164 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
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"fewshot_delimiter": "\n\n",
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|
173 |
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|
174 |
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{
|
175 |
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"metric": "exact_match",
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176 |
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177 |
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"higher_is_better": true
|
178 |
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}
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179 |
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],
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180 |
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"output_type": "generate_until",
|
181 |
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182 |
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183 |
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184 |
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],
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185 |
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|
186 |
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|
187 |
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},
|
188 |
+
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|
189 |
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"should_decontaminate": false,
|
190 |
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"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
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"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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|
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|
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{
|
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|
218 |
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"higher_is_better": true
|
219 |
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}
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],
|
221 |
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"output_type": "generate_until",
|
222 |
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"generation_kwargs": {
|
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"until": [
|
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],
|
226 |
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|
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|
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},
|
229 |
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|
230 |
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|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
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"description": "",
|
252 |
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"target_delimiter": " ",
|
253 |
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"fewshot_delimiter": "\n\n",
|
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|
255 |
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|
256 |
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{
|
257 |
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"metric": "exact_match",
|
258 |
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
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"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
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],
|
267 |
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"do_sample": false,
|
268 |
+
"temperature": 0.0
|
269 |
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},
|
270 |
+
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|
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|
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|
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}
|
pythia-14m-seed1/step32000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-29-41.037818.json
ADDED
@@ -0,0 +1,482 @@
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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}
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],
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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|
324 |
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|
325 |
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|
326 |
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|
327 |
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|
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|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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|
334 |
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335 |
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|
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{
|
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|
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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|
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|
346 |
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|
347 |
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|
348 |
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|
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|
356 |
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|
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361 |
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|
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],
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|
367 |
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369 |
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371 |
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|
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|
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"date": 1723476355.902467,
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}
|
pythia-14m-seed1/step33000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-33-49.879234.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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|
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|
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|
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|
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|
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|
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|
21 |
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|
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|
24 |
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|
26 |
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|
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|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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|
32 |
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|
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|
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|
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|
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|
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pythia-14m-seed1/step35000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-42-22.390553.json
ADDED
@@ -0,0 +1,482 @@
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{
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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{
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}
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"output_type": "generate_until",
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"metadata": {
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"task": "hendrycks_math_counting_and_prob",
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"transformers_version": "4.40.2",
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"upper_git_hash": null,
|
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"task_hashes": {},
|
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"model_source": "hf",
|
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"model_name": "EleutherAI/pythia-14m-seed1",
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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"start_time": 597710.476242525,
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}
|
pythia-14m-seed1/step36000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-46-35.988527.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
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"perplexity,none": 1421.4078171093663,
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"perplexity_stderr,none": 62.81381033079262,
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"alias": "hendrycks_math"
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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28 |
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"alias": " - hendrycks_math_geometry"
|
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},
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
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},
|
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
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}
|
50 |
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},
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51 |
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"groups": {
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"hendrycks_math": {
|
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|
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|
55 |
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"alias": "hendrycks_math"
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56 |
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}
|
57 |
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},
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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"until": [
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101 |
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"Problem:"
|
102 |
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],
|
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"do_sample": false,
|
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"temperature": 0.0
|
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},
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
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"dataset_name": "counting_and_probability",
|
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"dataset_kwargs": {
|
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|
121 |
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},
|
122 |
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"training_split": "train",
|
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
+
"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
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}
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pythia-14m-seed1/step37000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-50-44.879723.json
ADDED
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1 |
+
{
|
2 |
+
"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1217.9659264756383,
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5 |
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"perplexity_stderr,none": 55.009084692314545,
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"acc,none": 0.09994178148651271,
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"acc_stderr,none": 0.004178504912005431,
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"alias": "lambada_openai"
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},
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"hendrycks_math": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"hendrycks_math_counting_and_prob": {
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
26 |
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"exact_match,none": 0.0,
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
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43 |
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"alias": " - hendrycks_math_prealgebra"
|
44 |
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
|
53 |
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"exact_match,none": 0.0,
|
54 |
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"exact_match_stderr,none": 0.0,
|
55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
82 |
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
+
"doc_to_target": "{{answer}}",
|
86 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
92 |
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{
|
93 |
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"metric": "exact_match",
|
94 |
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
104 |
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"temperature": 0.0
|
105 |
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},
|
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"repeats": 1,
|
107 |
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
109 |
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
123 |
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
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131 |
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"num_fewshot": 0,
|
132 |
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"metric_list": [
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133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
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142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
+
"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
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"fewshot_delimiter": "\n\n",
|
172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
+
}
|
179 |
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],
|
180 |
+
"output_type": "generate_until",
|
181 |
+
"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
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"temperature": 0.0
|
187 |
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},
|
188 |
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
+
},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
+
"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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pythia-14m-seed1/step38000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-54-53.146841.json
ADDED
@@ -0,0 +1,482 @@
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1 |
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{
|
2 |
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"results": {
|
3 |
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|
4 |
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5 |
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6 |
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8 |
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9 |
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},
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10 |
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11 |
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12 |
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|
13 |
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14 |
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},
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15 |
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|
16 |
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17 |
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18 |
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19 |
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},
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20 |
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21 |
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22 |
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23 |
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24 |
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},
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25 |
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|
26 |
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|
27 |
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"exact_match_stderr,none": 0.0,
|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
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},
|
30 |
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|
31 |
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|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
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35 |
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|
36 |
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"exact_match,none": 0.0,
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37 |
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38 |
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"alias": " - hendrycks_math_num_theory"
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39 |
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},
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40 |
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42 |
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43 |
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44 |
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},
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49 |
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}
|
50 |
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},
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|
56 |
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}
|
57 |
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},
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58 |
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|
60 |
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|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
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"hendrycks_math_algebra"
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],
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"lambada_openai": []
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},
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70 |
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71 |
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"task": "hendrycks_math_algebra",
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73 |
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"group": [
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74 |
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|
75 |
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],
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76 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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85 |
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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],
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},
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}
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"task": "hendrycks_math_counting_and_prob",
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114 |
+
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|
115 |
+
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|
116 |
+
],
|
117 |
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|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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{
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141 |
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],
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},
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|
148 |
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|
149 |
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"metadata": {
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|
151 |
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}
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152 |
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},
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153 |
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"task": "hendrycks_math_geometry",
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155 |
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"group": [
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156 |
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"math_word_problems"
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157 |
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],
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158 |
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160 |
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|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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|
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|
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{
|
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"metric": "exact_match",
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177 |
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"higher_is_better": true
|
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}
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179 |
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],
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180 |
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181 |
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182 |
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],
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|
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|
187 |
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},
|
188 |
+
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|
189 |
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"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
+
],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
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"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
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"description": "",
|
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|
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
218 |
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"higher_is_better": true
|
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}
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],
|
221 |
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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|
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],
|
226 |
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|
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|
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},
|
229 |
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|
230 |
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|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
+
"description": "",
|
252 |
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
|
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|
255 |
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|
256 |
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{
|
257 |
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"metric": "exact_match",
|
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"aggregation": "mean",
|
259 |
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"higher_is_better": true
|
260 |
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}
|
261 |
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],
|
262 |
+
"output_type": "generate_until",
|
263 |
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"generation_kwargs": {
|
264 |
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"until": [
|
265 |
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"Problem:"
|
266 |
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],
|
267 |
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"do_sample": false,
|
268 |
+
"temperature": 0.0
|
269 |
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|
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|
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}
|
pythia-14m-seed1/step39000/EleutherAI__pythia-14m-seed1/results_2024-08-12T08-59-06.449694.json
ADDED
@@ -0,0 +1,482 @@
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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}
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},
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"task": "hendrycks_math_num_theory",
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],
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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323 |
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"dataset_name": "precalculus",
|
324 |
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|
325 |
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"trust_remote_code": true
|
326 |
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|
327 |
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|
328 |
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|
329 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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"doc_to_target": "{{answer}}",
|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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|
334 |
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"target_delimiter": " ",
|
335 |
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336 |
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337 |
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|
338 |
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{
|
339 |
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|
340 |
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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"output_type": "generate_until",
|
345 |
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|
346 |
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"until": [
|
347 |
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|
348 |
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|
349 |
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|
350 |
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|
351 |
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|
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|
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|
354 |
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"metadata": {
|
355 |
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|
356 |
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}
|
357 |
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},
|
358 |
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"lambada_openai": {
|
359 |
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"task": "lambada_openai",
|
360 |
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"group": [
|
361 |
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|
362 |
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],
|
363 |
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|
364 |
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365 |
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"dataset_kwargs": {
|
366 |
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"trust_remote_code": true
|
367 |
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|
368 |
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"test_split": "test",
|
369 |
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"doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
|
370 |
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"doc_to_target": "{{' '+text.split(' ')[-1]}}",
|
371 |
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"description": "",
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|
380 |
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|
381 |
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|
382 |
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|
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|
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|
385 |
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|
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|
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|
390 |
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|
391 |
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|
392 |
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|
393 |
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|
394 |
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|
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|
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|
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},
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"git_hash": "51a7ca9",
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"date": 1723478118.404584,
|
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"task_hashes": {},
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"model_name": "EleutherAI/pythia-14m-seed1",
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"start_time": 598713.237571929,
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"end_time": 598958.656903778,
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"total_evaluation_time_seconds": "245.41933184897061"
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}
|
pythia-14m-seed1/step4/EleutherAI__pythia-14m-seed1/results_2024-08-12T05-47-23.908503.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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|
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|
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|
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12 |
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|
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|
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|
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|
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|
18 |
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|
19 |
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|
20 |
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|
21 |
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|
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|
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|
24 |
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|
25 |
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|
26 |
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"exact_match,none": 0.0,
|
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|
28 |
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"alias": " - hendrycks_math_geometry"
|
29 |
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|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
32 |
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"exact_match_stderr,none": 0.0,
|
33 |
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|
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|
35 |
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|
36 |
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|
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pythia-14m-seed1/step4000/EleutherAI__pythia-14m-seed1/results_2024-08-12T06-33-15.998610.json
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1 |
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{
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58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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|
61 |
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|
62 |
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|
63 |
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|
64 |
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|
65 |
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66 |
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|
67 |
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],
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68 |
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"lambada_openai": []
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
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72 |
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"task": "hendrycks_math_algebra",
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73 |
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"group": [
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74 |
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75 |
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],
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76 |
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"dataset_path": "EleutherAI/hendrycks_math",
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77 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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86 |
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pythia-14m-seed1/step40000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-03-22.077168.json
ADDED
@@ -0,0 +1,482 @@
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{
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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{
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}
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"output_type": "generate_until",
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"metadata": {
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}
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"task": "hendrycks_math_counting_and_prob",
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"transformers_version": "4.40.2",
|
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"upper_git_hash": null,
|
475 |
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"task_hashes": {},
|
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"model_source": "hf",
|
477 |
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"model_name": "EleutherAI/pythia-14m-seed1",
|
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"model_name_sanitized": "EleutherAI__pythia-14m-seed1",
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479 |
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"start_time": 598965.37537771,
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}
|
pythia-14m-seed1/step41000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-07-29.805237.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
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3 |
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"lambada_openai": {
|
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"perplexity,none": 1066.3853643932846,
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"perplexity_stderr,none": 48.51192891663625,
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"alias": "hendrycks_math"
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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},
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
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"exact_match,none": 0.0020876826722338203,
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"exact_match_stderr,none": 0.0020876826722338207,
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"alias": " - hendrycks_math_geometry"
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},
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
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32 |
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"exact_match_stderr,none": 0.0,
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33 |
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"alias": " - hendrycks_math_intermediate_algebra"
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},
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
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37 |
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
39 |
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},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
|
42 |
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"exact_match_stderr,none": 0.0,
|
43 |
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"alias": " - hendrycks_math_prealgebra"
|
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},
|
45 |
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"hendrycks_math_precalc": {
|
46 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
48 |
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"alias": " - hendrycks_math_precalc"
|
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}
|
50 |
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},
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51 |
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"groups": {
|
52 |
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"hendrycks_math": {
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"exact_match,none": 0.00019999999999999998,
|
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"exact_match_stderr,none": 0.00019993112346228413,
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55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
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},
|
70 |
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"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
78 |
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"dataset_kwargs": {
|
79 |
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"trust_remote_code": true
|
80 |
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},
|
81 |
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"training_split": "train",
|
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"test_split": "test",
|
83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
87 |
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"description": "",
|
88 |
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"target_delimiter": " ",
|
89 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
91 |
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"metric_list": [
|
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{
|
93 |
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"metric": "exact_match",
|
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"aggregation": "mean",
|
95 |
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"higher_is_better": true
|
96 |
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}
|
97 |
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],
|
98 |
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"output_type": "generate_until",
|
99 |
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"generation_kwargs": {
|
100 |
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"until": [
|
101 |
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"Problem:"
|
102 |
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],
|
103 |
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"do_sample": false,
|
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"temperature": 0.0
|
105 |
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},
|
106 |
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"repeats": 1,
|
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"should_decontaminate": false,
|
108 |
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"metadata": {
|
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"version": 1.0
|
110 |
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}
|
111 |
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},
|
112 |
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
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"dataset_path": "EleutherAI/hendrycks_math",
|
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"dataset_name": "counting_and_probability",
|
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
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"test_split": "test",
|
124 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
129 |
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"target_delimiter": " ",
|
130 |
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
132 |
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"metric_list": [
|
133 |
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{
|
134 |
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"metric": "exact_match",
|
135 |
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
137 |
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}
|
138 |
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],
|
139 |
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
|
141 |
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"until": [
|
142 |
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"Problem:"
|
143 |
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],
|
144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
150 |
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
|
158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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482 |
+
}
|
pythia-14m-seed1/step42000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-11-40.178060.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
+
{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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"perplexity,none": 1120.2234090661466,
|
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"perplexity_stderr,none": 50.333654631556755,
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"alias": "hendrycks_math"
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},
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"hendrycks_math_algebra": {
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"exact_match,none": 0.0,
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"alias": " - hendrycks_math_algebra"
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"alias": " - hendrycks_math_counting_and_prob"
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},
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"hendrycks_math_geometry": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_geometry"
|
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},
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"hendrycks_math_intermediate_algebra": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
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"alias": " - hendrycks_math_intermediate_algebra"
|
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},
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"hendrycks_math_num_theory": {
|
36 |
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
|
38 |
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"alias": " - hendrycks_math_num_theory"
|
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},
|
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"hendrycks_math_prealgebra": {
|
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"exact_match,none": 0.0,
|
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"exact_match_stderr,none": 0.0,
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"alias": " - hendrycks_math_prealgebra"
|
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},
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|
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"exact_match_stderr,none": 0.0,
|
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"alias": " - hendrycks_math_precalc"
|
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}
|
50 |
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},
|
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"groups": {
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"hendrycks_math": {
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|
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|
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"alias": "hendrycks_math"
|
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}
|
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},
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"group_subtasks": {
|
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
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"hendrycks_math_prealgebra",
|
62 |
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"hendrycks_math_num_theory",
|
63 |
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"hendrycks_math_intermediate_algebra",
|
64 |
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"hendrycks_math_geometry",
|
65 |
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"hendrycks_math_counting_and_prob",
|
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"hendrycks_math_algebra"
|
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],
|
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"lambada_openai": []
|
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},
|
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"configs": {
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"hendrycks_math_algebra": {
|
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"task": "hendrycks_math_algebra",
|
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
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"dataset_path": "EleutherAI/hendrycks_math",
|
77 |
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"dataset_name": "algebra",
|
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"dataset_kwargs": {
|
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"trust_remote_code": true
|
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},
|
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"training_split": "train",
|
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"test_split": "test",
|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
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"metric_list": [
|
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{
|
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"metric": "exact_match",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
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}
|
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],
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"output_type": "generate_until",
|
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"generation_kwargs": {
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"until": [
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"Problem:"
|
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],
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|
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"temperature": 0.0
|
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},
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
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"metadata": {
|
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"version": 1.0
|
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}
|
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},
|
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"hendrycks_math_counting_and_prob": {
|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
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"group": [
|
115 |
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"math_word_problems"
|
116 |
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],
|
117 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
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"dataset_name": "counting_and_probability",
|
119 |
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"dataset_kwargs": {
|
120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
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"test_split": "test",
|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
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"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
128 |
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
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"metric_list": [
|
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{
|
134 |
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"metric": "exact_match",
|
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"aggregation": "mean",
|
136 |
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"higher_is_better": true
|
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}
|
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],
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"output_type": "generate_until",
|
140 |
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"generation_kwargs": {
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"until": [
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|
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],
|
144 |
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|
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|
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},
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
149 |
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"metadata": {
|
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"version": 1.0
|
151 |
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}
|
152 |
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},
|
153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
|
155 |
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"group": [
|
156 |
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"math_word_problems"
|
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],
|
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"dataset_path": "EleutherAI/hendrycks_math",
|
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"dataset_name": "geometry",
|
160 |
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"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
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},
|
163 |
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"training_split": "train",
|
164 |
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"test_split": "test",
|
165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
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"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
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"description": "",
|
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
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"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
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"higher_is_better": true
|
178 |
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}
|
179 |
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],
|
180 |
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"output_type": "generate_until",
|
181 |
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"generation_kwargs": {
|
182 |
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"until": [
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183 |
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"Problem:"
|
184 |
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],
|
185 |
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|
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|
187 |
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},
|
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"repeats": 1,
|
189 |
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"should_decontaminate": false,
|
190 |
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"metadata": {
|
191 |
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"version": 1.0
|
192 |
+
}
|
193 |
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},
|
194 |
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"hendrycks_math_intermediate_algebra": {
|
195 |
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"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
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"math_word_problems"
|
198 |
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],
|
199 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
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"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
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"trust_remote_code": true
|
203 |
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},
|
204 |
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"training_split": "train",
|
205 |
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"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
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"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
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"description": "",
|
211 |
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"target_delimiter": " ",
|
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pythia-14m-seed1/step43000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-15-48.237659.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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|
3 |
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"lambada_openai": {
|
4 |
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5 |
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6 |
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7 |
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8 |
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9 |
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},
|
10 |
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|
11 |
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"exact_match,none": 0.0002,
|
12 |
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"exact_match_stderr,none": 0.00019995678465298491,
|
13 |
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"alias": "hendrycks_math"
|
14 |
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},
|
15 |
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|
16 |
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"exact_match,none": 0.0,
|
17 |
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"exact_match_stderr,none": 0.0,
|
18 |
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"alias": " - hendrycks_math_algebra"
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19 |
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},
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20 |
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|
21 |
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|
22 |
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23 |
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"alias": " - hendrycks_math_counting_and_prob"
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24 |
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},
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25 |
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|
26 |
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"exact_match,none": 0.0,
|
27 |
+
"exact_match_stderr,none": 0.0,
|
28 |
+
"alias": " - hendrycks_math_geometry"
|
29 |
+
},
|
30 |
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|
31 |
+
"exact_match,none": 0.0,
|
32 |
+
"exact_match_stderr,none": 0.0,
|
33 |
+
"alias": " - hendrycks_math_intermediate_algebra"
|
34 |
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},
|
35 |
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|
36 |
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"exact_match,none": 0.0,
|
37 |
+
"exact_match_stderr,none": 0.0,
|
38 |
+
"alias": " - hendrycks_math_num_theory"
|
39 |
+
},
|
40 |
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"hendrycks_math_prealgebra": {
|
41 |
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"exact_match,none": 0.0,
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42 |
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"exact_match_stderr,none": 0.0,
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43 |
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"alias": " - hendrycks_math_prealgebra"
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44 |
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},
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46 |
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47 |
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|
48 |
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"alias": " - hendrycks_math_precalc"
|
49 |
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}
|
50 |
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},
|
51 |
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"groups": {
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52 |
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"hendrycks_math": {
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54 |
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55 |
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"alias": "hendrycks_math"
|
56 |
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}
|
57 |
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},
|
58 |
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"group_subtasks": {
|
59 |
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"hendrycks_math": [
|
60 |
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"hendrycks_math_precalc",
|
61 |
+
"hendrycks_math_prealgebra",
|
62 |
+
"hendrycks_math_num_theory",
|
63 |
+
"hendrycks_math_intermediate_algebra",
|
64 |
+
"hendrycks_math_geometry",
|
65 |
+
"hendrycks_math_counting_and_prob",
|
66 |
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"hendrycks_math_algebra"
|
67 |
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],
|
68 |
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"lambada_openai": []
|
69 |
+
},
|
70 |
+
"configs": {
|
71 |
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"hendrycks_math_algebra": {
|
72 |
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"task": "hendrycks_math_algebra",
|
73 |
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"group": [
|
74 |
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"math_word_problems"
|
75 |
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],
|
76 |
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"dataset_path": "EleutherAI/hendrycks_math",
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77 |
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78 |
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|
79 |
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80 |
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},
|
81 |
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"training_split": "train",
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82 |
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83 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
84 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
85 |
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"doc_to_target": "{{answer}}",
|
86 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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{
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99 |
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|
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|
102 |
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],
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103 |
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|
104 |
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"temperature": 0.0
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105 |
+
},
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106 |
+
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|
107 |
+
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|
108 |
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"metadata": {
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"version": 1.0
|
110 |
+
}
|
111 |
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},
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112 |
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|
113 |
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"task": "hendrycks_math_counting_and_prob",
|
114 |
+
"group": [
|
115 |
+
"math_word_problems"
|
116 |
+
],
|
117 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
118 |
+
"dataset_name": "counting_and_probability",
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119 |
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"dataset_kwargs": {
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120 |
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"trust_remote_code": true
|
121 |
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},
|
122 |
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"training_split": "train",
|
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124 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
125 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
126 |
+
"doc_to_target": "{{answer}}",
|
127 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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128 |
+
"description": "",
|
129 |
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"target_delimiter": " ",
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130 |
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"fewshot_delimiter": "\n\n",
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132 |
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"metric_list": [
|
133 |
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{
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134 |
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"metric": "exact_match",
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135 |
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"aggregation": "mean",
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137 |
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}
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138 |
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],
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139 |
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140 |
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141 |
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"until": [
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142 |
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"Problem:"
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143 |
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],
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144 |
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"do_sample": false,
|
145 |
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"temperature": 0.0
|
146 |
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},
|
147 |
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"repeats": 1,
|
148 |
+
"should_decontaminate": false,
|
149 |
+
"metadata": {
|
150 |
+
"version": 1.0
|
151 |
+
}
|
152 |
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},
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153 |
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"hendrycks_math_geometry": {
|
154 |
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"task": "hendrycks_math_geometry",
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155 |
+
"group": [
|
156 |
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"math_word_problems"
|
157 |
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],
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158 |
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"dataset_path": "EleutherAI/hendrycks_math",
|
159 |
+
"dataset_name": "geometry",
|
160 |
+
"dataset_kwargs": {
|
161 |
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"trust_remote_code": true
|
162 |
+
},
|
163 |
+
"training_split": "train",
|
164 |
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"test_split": "test",
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165 |
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
166 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
167 |
+
"doc_to_target": "{{answer}}",
|
168 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
169 |
+
"description": "",
|
170 |
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"target_delimiter": " ",
|
171 |
+
"fewshot_delimiter": "\n\n",
|
172 |
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"num_fewshot": 0,
|
173 |
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"metric_list": [
|
174 |
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{
|
175 |
+
"metric": "exact_match",
|
176 |
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"aggregation": "mean",
|
177 |
+
"higher_is_better": true
|
178 |
+
}
|
179 |
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],
|
180 |
+
"output_type": "generate_until",
|
181 |
+
"generation_kwargs": {
|
182 |
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"until": [
|
183 |
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"Problem:"
|
184 |
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],
|
185 |
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"do_sample": false,
|
186 |
+
"temperature": 0.0
|
187 |
+
},
|
188 |
+
"repeats": 1,
|
189 |
+
"should_decontaminate": false,
|
190 |
+
"metadata": {
|
191 |
+
"version": 1.0
|
192 |
+
}
|
193 |
+
},
|
194 |
+
"hendrycks_math_intermediate_algebra": {
|
195 |
+
"task": "hendrycks_math_intermediate_algebra",
|
196 |
+
"group": [
|
197 |
+
"math_word_problems"
|
198 |
+
],
|
199 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
200 |
+
"dataset_name": "intermediate_algebra",
|
201 |
+
"dataset_kwargs": {
|
202 |
+
"trust_remote_code": true
|
203 |
+
},
|
204 |
+
"training_split": "train",
|
205 |
+
"test_split": "test",
|
206 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
207 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
208 |
+
"doc_to_target": "{{answer}}",
|
209 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
210 |
+
"description": "",
|
211 |
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"target_delimiter": " ",
|
212 |
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"fewshot_delimiter": "\n\n",
|
213 |
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"num_fewshot": 0,
|
214 |
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"metric_list": [
|
215 |
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{
|
216 |
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"metric": "exact_match",
|
217 |
+
"aggregation": "mean",
|
218 |
+
"higher_is_better": true
|
219 |
+
}
|
220 |
+
],
|
221 |
+
"output_type": "generate_until",
|
222 |
+
"generation_kwargs": {
|
223 |
+
"until": [
|
224 |
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"Problem:"
|
225 |
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],
|
226 |
+
"do_sample": false,
|
227 |
+
"temperature": 0.0
|
228 |
+
},
|
229 |
+
"repeats": 1,
|
230 |
+
"should_decontaminate": false,
|
231 |
+
"metadata": {
|
232 |
+
"version": 1.0
|
233 |
+
}
|
234 |
+
},
|
235 |
+
"hendrycks_math_num_theory": {
|
236 |
+
"task": "hendrycks_math_num_theory",
|
237 |
+
"group": [
|
238 |
+
"math_word_problems"
|
239 |
+
],
|
240 |
+
"dataset_path": "EleutherAI/hendrycks_math",
|
241 |
+
"dataset_name": "number_theory",
|
242 |
+
"dataset_kwargs": {
|
243 |
+
"trust_remote_code": true
|
244 |
+
},
|
245 |
+
"training_split": "train",
|
246 |
+
"test_split": "test",
|
247 |
+
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
248 |
+
"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
249 |
+
"doc_to_target": "{{answer}}",
|
250 |
+
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
251 |
+
"description": "",
|
252 |
+
"target_delimiter": " ",
|
253 |
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"fewshot_delimiter": "\n\n",
|
254 |
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"num_fewshot": 0,
|
255 |
+
"metric_list": [
|
256 |
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{
|
257 |
+
"metric": "exact_match",
|
258 |
+
"aggregation": "mean",
|
259 |
+
"higher_is_better": true
|
260 |
+
}
|
261 |
+
],
|
262 |
+
"output_type": "generate_until",
|
263 |
+
"generation_kwargs": {
|
264 |
+
"until": [
|
265 |
+
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|
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}
|
pythia-14m-seed1/step44000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-20-11.460936.json
ADDED
@@ -0,0 +1,482 @@
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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|
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}
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},
|
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|
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"task": "hendrycks_math_num_theory",
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],
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|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
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"doc_to_target": "{{answer}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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}
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],
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|
320 |
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],
|
322 |
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|
323 |
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|
324 |
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|
325 |
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|
326 |
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|
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
330 |
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"doc_to_text": "Problem: {{problem}}\nAnswer:",
|
331 |
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|
332 |
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
|
333 |
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334 |
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|
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|
341 |
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|
342 |
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|
343 |
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344 |
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|
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|
346 |
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|
347 |
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|
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|
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361 |
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|
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],
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|
367 |
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371 |
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|
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"date": 1723479380.6053634,
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}
|
pythia-14m-seed1/step45000/EleutherAI__pythia-14m-seed1/results_2024-08-12T09-24-41.702741.json
ADDED
@@ -0,0 +1,482 @@
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|
1 |
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{
|
2 |
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"results": {
|
3 |
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"lambada_openai": {
|
4 |
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|
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|
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|
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|
18 |
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|
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|
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|
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|
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|
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|
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"alias": " - hendrycks_math_geometry"
|
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|
30 |
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"hendrycks_math_intermediate_algebra": {
|
31 |
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"exact_match,none": 0.0,
|
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|
33 |
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|
34 |
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|
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