Add evaluation
#8
by
Muennighoff
- opened
README.md
CHANGED
@@ -50,6 +50,1546 @@ language:
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pipeline_tag: text-generation
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|
53 |
---
|
54 |
|
55 |
<h1 style='text-align: center '>BLOOM LM</h1>
|
@@ -453,7 +1993,7 @@ Includes:
|
|
453 |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
|
454 |
|
455 |
### Factors
|
456 |
-
*This section lists some different aspects of
|
457 |
|
458 |
- Language, such as English or Yoruba
|
459 |
|
@@ -464,6 +2004,154 @@ And multiple different metrics for specific tasks. _(More evaluation metrics for
|
|
464 |
### Results
|
465 |
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
|
466 |
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|
467 |
**Train-time Evaluation:**
|
468 |
|
469 |
As of 25.May.2022, 15:00 PST:
|
@@ -474,8 +2162,6 @@ As of 25.May.2022, 15:00 PST:
|
|
474 |
|
475 |
- Perplexity: 8.9
|
476 |
|
477 |
-
(More evaluation scores forthcoming at the end of model training.)
|
478 |
-
|
479 |
</details>
|
480 |
<p> </p>
|
481 |
|
@@ -561,5 +2247,5 @@ Initial prompting experiments using interim checkpoints: https://huggingface.co/
|
|
561 |
## Model Card Authors
|
562 |
*Ordered roughly chronologically and by amount of time spent.*
|
563 |
|
564 |
-
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay
|
565 |
|
|
|
50 |
- zht
|
51 |
- zu
|
52 |
pipeline_tag: text-generation
|
53 |
+
model-index:
|
54 |
+
- name: bloom
|
55 |
+
results:
|
56 |
+
- task:
|
57 |
+
type: text-generation
|
58 |
+
name: text generation
|
59 |
+
dataset:
|
60 |
+
name: arc_challenge
|
61 |
+
type: arc_challenge
|
62 |
+
metrics:
|
63 |
+
- name: acc
|
64 |
+
type: acc
|
65 |
+
value: 0.27986348122866894
|
66 |
+
verified: false
|
67 |
+
- task:
|
68 |
+
type: text-generation
|
69 |
+
name: text generation
|
70 |
+
dataset:
|
71 |
+
name: arc_easy
|
72 |
+
type: arc_easy
|
73 |
+
metrics:
|
74 |
+
- name: acc
|
75 |
+
type: acc
|
76 |
+
value: 0.5946969696969697
|
77 |
+
verified: false
|
78 |
+
- task:
|
79 |
+
type: text-generation
|
80 |
+
name: text generation
|
81 |
+
dataset:
|
82 |
+
name: axb
|
83 |
+
type: axb
|
84 |
+
metrics:
|
85 |
+
- name: acc
|
86 |
+
type: acc
|
87 |
+
value: 0.4433876811594203
|
88 |
+
verified: false
|
89 |
+
- task:
|
90 |
+
type: text-generation
|
91 |
+
name: text generation
|
92 |
+
dataset:
|
93 |
+
name: axg
|
94 |
+
type: axg
|
95 |
+
metrics:
|
96 |
+
- name: acc
|
97 |
+
type: acc
|
98 |
+
value: 0.5
|
99 |
+
verified: false
|
100 |
+
- task:
|
101 |
+
type: text-generation
|
102 |
+
name: text generation
|
103 |
+
dataset:
|
104 |
+
name: boolq
|
105 |
+
type: boolq
|
106 |
+
metrics:
|
107 |
+
- name: acc
|
108 |
+
type: acc
|
109 |
+
value: 0.6165137614678899
|
110 |
+
verified: false
|
111 |
+
- task:
|
112 |
+
type: text-generation
|
113 |
+
name: text generation
|
114 |
+
dataset:
|
115 |
+
name: cb
|
116 |
+
type: cb
|
117 |
+
metrics:
|
118 |
+
- name: acc
|
119 |
+
type: acc
|
120 |
+
value: 0.30357142857142855
|
121 |
+
verified: false
|
122 |
+
- task:
|
123 |
+
type: text-generation
|
124 |
+
name: text generation
|
125 |
+
dataset:
|
126 |
+
name: cola
|
127 |
+
type: cola
|
128 |
+
metrics:
|
129 |
+
- name: acc
|
130 |
+
type: acc
|
131 |
+
value: 0.610738255033557
|
132 |
+
verified: false
|
133 |
+
- task:
|
134 |
+
type: text-generation
|
135 |
+
name: text generation
|
136 |
+
dataset:
|
137 |
+
name: copa
|
138 |
+
type: copa
|
139 |
+
metrics:
|
140 |
+
- name: acc
|
141 |
+
type: acc
|
142 |
+
value: 0.63
|
143 |
+
verified: false
|
144 |
+
- task:
|
145 |
+
type: text-generation
|
146 |
+
name: text generation
|
147 |
+
dataset:
|
148 |
+
name: crows_pairs_english
|
149 |
+
type: crows_pairs_english
|
150 |
+
metrics:
|
151 |
+
- name: acc
|
152 |
+
type: acc
|
153 |
+
value: 0.4973166368515206
|
154 |
+
verified: false
|
155 |
+
- task:
|
156 |
+
type: text-generation
|
157 |
+
name: text generation
|
158 |
+
dataset:
|
159 |
+
name: crows_pairs_french
|
160 |
+
type: crows_pairs_french
|
161 |
+
metrics:
|
162 |
+
- name: acc
|
163 |
+
type: acc
|
164 |
+
value: 0.5032796660703638
|
165 |
+
verified: false
|
166 |
+
- task:
|
167 |
+
type: text-generation
|
168 |
+
name: text generation
|
169 |
+
dataset:
|
170 |
+
name: diabla
|
171 |
+
type: diabla
|
172 |
+
metrics:
|
173 |
+
- name: acc
|
174 |
+
type: acc
|
175 |
+
value: 0.28888308977035493
|
176 |
+
verified: false
|
177 |
+
- task:
|
178 |
+
type: text-generation
|
179 |
+
name: text generation
|
180 |
+
dataset:
|
181 |
+
name: gsarti/flores_101_afr
|
182 |
+
type: gsarti/flores_101_afr
|
183 |
+
metrics:
|
184 |
+
- name: byte_perplexity
|
185 |
+
type: byte_perplexity
|
186 |
+
value: 6.500798737976343
|
187 |
+
verified: false
|
188 |
+
- task:
|
189 |
+
type: text-generation
|
190 |
+
name: text generation
|
191 |
+
dataset:
|
192 |
+
name: gsarti/flores_101_amh
|
193 |
+
type: gsarti/flores_101_amh
|
194 |
+
metrics:
|
195 |
+
- name: byte_perplexity
|
196 |
+
type: byte_perplexity
|
197 |
+
value: 3.9726863338897145
|
198 |
+
verified: false
|
199 |
+
- task:
|
200 |
+
type: text-generation
|
201 |
+
name: text generation
|
202 |
+
dataset:
|
203 |
+
name: gsarti/flores_101_ara
|
204 |
+
type: gsarti/flores_101_ara
|
205 |
+
metrics:
|
206 |
+
- name: byte_perplexity
|
207 |
+
type: byte_perplexity
|
208 |
+
value: 1.8083841089875814
|
209 |
+
verified: false
|
210 |
+
- task:
|
211 |
+
type: text-generation
|
212 |
+
name: text generation
|
213 |
+
dataset:
|
214 |
+
name: gsarti/flores_101_asm
|
215 |
+
type: gsarti/flores_101_asm
|
216 |
+
metrics:
|
217 |
+
- name: byte_perplexity
|
218 |
+
type: byte_perplexity
|
219 |
+
value: 5.699102962086425
|
220 |
+
verified: false
|
221 |
+
- task:
|
222 |
+
type: text-generation
|
223 |
+
name: text generation
|
224 |
+
dataset:
|
225 |
+
name: gsarti/flores_101_ast
|
226 |
+
type: gsarti/flores_101_ast
|
227 |
+
metrics:
|
228 |
+
- name: byte_perplexity
|
229 |
+
type: byte_perplexity
|
230 |
+
value: 3.9252047073429384
|
231 |
+
verified: false
|
232 |
+
- task:
|
233 |
+
type: text-generation
|
234 |
+
name: text generation
|
235 |
+
dataset:
|
236 |
+
name: gsarti/flores_101_azj
|
237 |
+
type: gsarti/flores_101_azj
|
238 |
+
metrics:
|
239 |
+
- name: byte_perplexity
|
240 |
+
type: byte_perplexity
|
241 |
+
value: 6.942805054270002
|
242 |
+
verified: false
|
243 |
+
- task:
|
244 |
+
type: text-generation
|
245 |
+
name: text generation
|
246 |
+
dataset:
|
247 |
+
name: gsarti/flores_101_bel
|
248 |
+
type: gsarti/flores_101_bel
|
249 |
+
metrics:
|
250 |
+
- name: byte_perplexity
|
251 |
+
type: byte_perplexity
|
252 |
+
value: 3.614136245847082
|
253 |
+
verified: false
|
254 |
+
- task:
|
255 |
+
type: text-generation
|
256 |
+
name: text generation
|
257 |
+
dataset:
|
258 |
+
name: gsarti/flores_101_ben
|
259 |
+
type: gsarti/flores_101_ben
|
260 |
+
metrics:
|
261 |
+
- name: byte_perplexity
|
262 |
+
type: byte_perplexity
|
263 |
+
value: 5.121491534300969
|
264 |
+
verified: false
|
265 |
+
- task:
|
266 |
+
type: text-generation
|
267 |
+
name: text generation
|
268 |
+
dataset:
|
269 |
+
name: gsarti/flores_101_bos
|
270 |
+
type: gsarti/flores_101_bos
|
271 |
+
metrics:
|
272 |
+
- name: byte_perplexity
|
273 |
+
type: byte_perplexity
|
274 |
+
value: 5.653353469118798
|
275 |
+
verified: false
|
276 |
+
- task:
|
277 |
+
type: text-generation
|
278 |
+
name: text generation
|
279 |
+
dataset:
|
280 |
+
name: gsarti/flores_101_bul
|
281 |
+
type: gsarti/flores_101_bul
|
282 |
+
metrics:
|
283 |
+
- name: byte_perplexity
|
284 |
+
type: byte_perplexity
|
285 |
+
value: 2.7014693938055068
|
286 |
+
verified: false
|
287 |
+
- task:
|
288 |
+
type: text-generation
|
289 |
+
name: text generation
|
290 |
+
dataset:
|
291 |
+
name: gsarti/flores_101_cat
|
292 |
+
type: gsarti/flores_101_cat
|
293 |
+
metrics:
|
294 |
+
- name: byte_perplexity
|
295 |
+
type: byte_perplexity
|
296 |
+
value: 2.305190041967345
|
297 |
+
verified: false
|
298 |
+
- task:
|
299 |
+
type: text-generation
|
300 |
+
name: text generation
|
301 |
+
dataset:
|
302 |
+
name: gsarti/flores_101_ceb
|
303 |
+
type: gsarti/flores_101_ceb
|
304 |
+
metrics:
|
305 |
+
- name: byte_perplexity
|
306 |
+
type: byte_perplexity
|
307 |
+
value: 6.291000321323428
|
308 |
+
verified: false
|
309 |
+
- task:
|
310 |
+
type: text-generation
|
311 |
+
name: text generation
|
312 |
+
dataset:
|
313 |
+
name: gsarti/flores_101_ces
|
314 |
+
type: gsarti/flores_101_ces
|
315 |
+
metrics:
|
316 |
+
- name: byte_perplexity
|
317 |
+
type: byte_perplexity
|
318 |
+
value: 5.447322753586386
|
319 |
+
verified: false
|
320 |
+
- task:
|
321 |
+
type: text-generation
|
322 |
+
name: text generation
|
323 |
+
dataset:
|
324 |
+
name: gsarti/flores_101_ckb
|
325 |
+
type: gsarti/flores_101_ckb
|
326 |
+
metrics:
|
327 |
+
- name: byte_perplexity
|
328 |
+
type: byte_perplexity
|
329 |
+
value: 3.7255124939234765
|
330 |
+
verified: false
|
331 |
+
- task:
|
332 |
+
type: text-generation
|
333 |
+
name: text generation
|
334 |
+
dataset:
|
335 |
+
name: gsarti/flores_101_cym
|
336 |
+
type: gsarti/flores_101_cym
|
337 |
+
metrics:
|
338 |
+
- name: byte_perplexity
|
339 |
+
type: byte_perplexity
|
340 |
+
value: 12.539424151448149
|
341 |
+
verified: false
|
342 |
+
- task:
|
343 |
+
type: text-generation
|
344 |
+
name: text generation
|
345 |
+
dataset:
|
346 |
+
name: gsarti/flores_101_dan
|
347 |
+
type: gsarti/flores_101_dan
|
348 |
+
metrics:
|
349 |
+
- name: byte_perplexity
|
350 |
+
type: byte_perplexity
|
351 |
+
value: 5.183309001005672
|
352 |
+
verified: false
|
353 |
+
- task:
|
354 |
+
type: text-generation
|
355 |
+
name: text generation
|
356 |
+
dataset:
|
357 |
+
name: gsarti/flores_101_deu
|
358 |
+
type: gsarti/flores_101_deu
|
359 |
+
metrics:
|
360 |
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- task:
|
1223 |
+
type: text-generation
|
1224 |
+
name: text generation
|
1225 |
+
dataset:
|
1226 |
+
name: gsarti/flores_101_wol
|
1227 |
+
type: gsarti/flores_101_wol
|
1228 |
+
metrics:
|
1229 |
+
- name: byte_perplexity
|
1230 |
+
type: byte_perplexity
|
1231 |
+
value: 9.144285650306488
|
1232 |
+
verified: false
|
1233 |
+
- task:
|
1234 |
+
type: text-generation
|
1235 |
+
name: text generation
|
1236 |
+
dataset:
|
1237 |
+
name: gsarti/flores_101_xho
|
1238 |
+
type: gsarti/flores_101_xho
|
1239 |
+
metrics:
|
1240 |
+
- name: byte_perplexity
|
1241 |
+
type: byte_perplexity
|
1242 |
+
value: 7.403240538286952
|
1243 |
+
verified: false
|
1244 |
+
- task:
|
1245 |
+
type: text-generation
|
1246 |
+
name: text generation
|
1247 |
+
dataset:
|
1248 |
+
name: gsarti/flores_101_yor
|
1249 |
+
type: gsarti/flores_101_yor
|
1250 |
+
metrics:
|
1251 |
+
- name: byte_perplexity
|
1252 |
+
type: byte_perplexity
|
1253 |
+
value: 5.91272037551173
|
1254 |
+
verified: false
|
1255 |
+
- task:
|
1256 |
+
type: text-generation
|
1257 |
+
name: text generation
|
1258 |
+
dataset:
|
1259 |
+
name: gsarti/flores_101_zho_simpl
|
1260 |
+
type: gsarti/flores_101_zho_simpl
|
1261 |
+
metrics:
|
1262 |
+
- name: byte_perplexity
|
1263 |
+
type: byte_perplexity
|
1264 |
+
value: 2.2769070822768533
|
1265 |
+
verified: false
|
1266 |
+
- task:
|
1267 |
+
type: text-generation
|
1268 |
+
name: text generation
|
1269 |
+
dataset:
|
1270 |
+
name: gsarti/flores_101_zho_trad
|
1271 |
+
type: gsarti/flores_101_zho_trad
|
1272 |
+
metrics:
|
1273 |
+
- name: byte_perplexity
|
1274 |
+
type: byte_perplexity
|
1275 |
+
value: 2.5180582198242383
|
1276 |
+
verified: false
|
1277 |
+
- task:
|
1278 |
+
type: text-generation
|
1279 |
+
name: text generation
|
1280 |
+
dataset:
|
1281 |
+
name: gsarti/flores_101_zul
|
1282 |
+
type: gsarti/flores_101_zul
|
1283 |
+
metrics:
|
1284 |
+
- name: byte_perplexity
|
1285 |
+
type: byte_perplexity
|
1286 |
+
value: 8.53353320693145
|
1287 |
+
verified: false
|
1288 |
+
- task:
|
1289 |
+
type: text-generation
|
1290 |
+
name: text generation
|
1291 |
+
dataset:
|
1292 |
+
name: headqa
|
1293 |
+
type: headqa
|
1294 |
+
metrics:
|
1295 |
+
- name: acc
|
1296 |
+
type: acc
|
1297 |
+
value: 0.26440554339897887
|
1298 |
+
verified: false
|
1299 |
+
- task:
|
1300 |
+
type: text-generation
|
1301 |
+
name: text generation
|
1302 |
+
dataset:
|
1303 |
+
name: hellaswag
|
1304 |
+
type: hellaswag
|
1305 |
+
metrics:
|
1306 |
+
- name: acc
|
1307 |
+
type: acc
|
1308 |
+
value: 0.41236805417247563
|
1309 |
+
verified: false
|
1310 |
+
- task:
|
1311 |
+
type: text-generation
|
1312 |
+
name: text generation
|
1313 |
+
dataset:
|
1314 |
+
name: logiqa
|
1315 |
+
type: logiqa
|
1316 |
+
metrics:
|
1317 |
+
- name: acc
|
1318 |
+
type: acc
|
1319 |
+
value: 0.2073732718894009
|
1320 |
+
verified: false
|
1321 |
+
- task:
|
1322 |
+
type: text-generation
|
1323 |
+
name: text generation
|
1324 |
+
dataset:
|
1325 |
+
name: mathqa
|
1326 |
+
type: mathqa
|
1327 |
+
metrics:
|
1328 |
+
- name: acc
|
1329 |
+
type: acc
|
1330 |
+
value: 0.24958123953098826
|
1331 |
+
verified: false
|
1332 |
+
- task:
|
1333 |
+
type: text-generation
|
1334 |
+
name: text generation
|
1335 |
+
dataset:
|
1336 |
+
name: mc_taco
|
1337 |
+
type: mc_taco
|
1338 |
+
metrics:
|
1339 |
+
- name: em
|
1340 |
+
type: em
|
1341 |
+
value: 0.11936936936936937
|
1342 |
+
verified: false
|
1343 |
+
- task:
|
1344 |
+
type: text-generation
|
1345 |
+
name: text generation
|
1346 |
+
dataset:
|
1347 |
+
name: mnli
|
1348 |
+
type: mnli
|
1349 |
+
metrics:
|
1350 |
+
- name: acc
|
1351 |
+
type: acc
|
1352 |
+
value: 0.35496688741721855
|
1353 |
+
verified: false
|
1354 |
+
- task:
|
1355 |
+
type: text-generation
|
1356 |
+
name: text generation
|
1357 |
+
dataset:
|
1358 |
+
name: mnli_mismatched
|
1359 |
+
type: mnli_mismatched
|
1360 |
+
metrics:
|
1361 |
+
- name: acc
|
1362 |
+
type: acc
|
1363 |
+
value: 0.35211554109031734
|
1364 |
+
verified: false
|
1365 |
+
- task:
|
1366 |
+
type: text-generation
|
1367 |
+
name: text generation
|
1368 |
+
dataset:
|
1369 |
+
name: mrpc
|
1370 |
+
type: mrpc
|
1371 |
+
metrics:
|
1372 |
+
- name: acc
|
1373 |
+
type: acc
|
1374 |
+
value: 0.5857843137254902
|
1375 |
+
verified: false
|
1376 |
+
- task:
|
1377 |
+
type: text-generation
|
1378 |
+
name: text generation
|
1379 |
+
dataset:
|
1380 |
+
name: multirc
|
1381 |
+
type: multirc
|
1382 |
+
metrics:
|
1383 |
+
- name: acc
|
1384 |
+
type: acc
|
1385 |
+
value: 0.5375412541254125
|
1386 |
+
verified: false
|
1387 |
+
- task:
|
1388 |
+
type: text-generation
|
1389 |
+
name: text generation
|
1390 |
+
dataset:
|
1391 |
+
name: openbookqa
|
1392 |
+
type: openbookqa
|
1393 |
+
metrics:
|
1394 |
+
- name: acc
|
1395 |
+
type: acc
|
1396 |
+
value: 0.216
|
1397 |
+
verified: false
|
1398 |
+
- task:
|
1399 |
+
type: text-generation
|
1400 |
+
name: text generation
|
1401 |
+
dataset:
|
1402 |
+
name: piqa
|
1403 |
+
type: piqa
|
1404 |
+
metrics:
|
1405 |
+
- name: acc
|
1406 |
+
type: acc
|
1407 |
+
value: 0.7078346028291621
|
1408 |
+
verified: false
|
1409 |
+
- task:
|
1410 |
+
type: text-generation
|
1411 |
+
name: text generation
|
1412 |
+
dataset:
|
1413 |
+
name: prost
|
1414 |
+
type: prost
|
1415 |
+
metrics:
|
1416 |
+
- name: acc
|
1417 |
+
type: acc
|
1418 |
+
value: 0.22683603757472245
|
1419 |
+
verified: false
|
1420 |
+
- task:
|
1421 |
+
type: text-generation
|
1422 |
+
name: text generation
|
1423 |
+
dataset:
|
1424 |
+
name: pubmedqa
|
1425 |
+
type: pubmedqa
|
1426 |
+
metrics:
|
1427 |
+
- name: acc
|
1428 |
+
type: acc
|
1429 |
+
value: 0.616
|
1430 |
+
verified: false
|
1431 |
+
- task:
|
1432 |
+
type: text-generation
|
1433 |
+
name: text generation
|
1434 |
+
dataset:
|
1435 |
+
name: qnli
|
1436 |
+
type: qnli
|
1437 |
+
metrics:
|
1438 |
+
- name: acc
|
1439 |
+
type: acc
|
1440 |
+
value: 0.5072304594545122
|
1441 |
+
verified: false
|
1442 |
+
- task:
|
1443 |
+
type: text-generation
|
1444 |
+
name: text generation
|
1445 |
+
dataset:
|
1446 |
+
name: qqp
|
1447 |
+
type: qqp
|
1448 |
+
metrics:
|
1449 |
+
- name: acc
|
1450 |
+
type: acc
|
1451 |
+
value: 0.3842443729903537
|
1452 |
+
verified: false
|
1453 |
+
- task:
|
1454 |
+
type: text-generation
|
1455 |
+
name: text generation
|
1456 |
+
dataset:
|
1457 |
+
name: race
|
1458 |
+
type: race
|
1459 |
+
metrics:
|
1460 |
+
- name: acc
|
1461 |
+
type: acc
|
1462 |
+
value: 0.3521531100478469
|
1463 |
+
verified: false
|
1464 |
+
- task:
|
1465 |
+
type: text-generation
|
1466 |
+
name: text generation
|
1467 |
+
dataset:
|
1468 |
+
name: rte
|
1469 |
+
type: rte
|
1470 |
+
metrics:
|
1471 |
+
- name: acc
|
1472 |
+
type: acc
|
1473 |
+
value: 0.47653429602888087
|
1474 |
+
verified: false
|
1475 |
+
- task:
|
1476 |
+
type: text-generation
|
1477 |
+
name: text generation
|
1478 |
+
dataset:
|
1479 |
+
name: sciq
|
1480 |
+
type: sciq
|
1481 |
+
metrics:
|
1482 |
+
- name: acc
|
1483 |
+
type: acc
|
1484 |
+
value: 0.892
|
1485 |
+
verified: false
|
1486 |
+
- task:
|
1487 |
+
type: text-generation
|
1488 |
+
name: text generation
|
1489 |
+
dataset:
|
1490 |
+
name: sst
|
1491 |
+
type: sst
|
1492 |
+
metrics:
|
1493 |
+
- name: acc
|
1494 |
+
type: acc
|
1495 |
+
value: 0.5177752293577982
|
1496 |
+
verified: false
|
1497 |
+
- task:
|
1498 |
+
type: text-generation
|
1499 |
+
name: text generation
|
1500 |
+
dataset:
|
1501 |
+
name: triviaqa
|
1502 |
+
type: triviaqa
|
1503 |
+
metrics:
|
1504 |
+
- name: acc
|
1505 |
+
type: acc
|
1506 |
+
value: 0.041633518960487934
|
1507 |
+
verified: false
|
1508 |
+
- task:
|
1509 |
+
type: text-generation
|
1510 |
+
name: text generation
|
1511 |
+
dataset:
|
1512 |
+
name: tydiqa_primary
|
1513 |
+
type: tydiqa_primary
|
1514 |
+
metrics:
|
1515 |
+
- name: acc
|
1516 |
+
type: acc
|
1517 |
+
value: 0.3011337608795236
|
1518 |
+
verified: false
|
1519 |
+
- task:
|
1520 |
+
type: text-generation
|
1521 |
+
name: text generation
|
1522 |
+
dataset:
|
1523 |
+
name: webqs
|
1524 |
+
type: webqs
|
1525 |
+
metrics:
|
1526 |
+
- name: acc
|
1527 |
+
type: acc
|
1528 |
+
value: 0.01673228346456693
|
1529 |
+
verified: false
|
1530 |
+
- task:
|
1531 |
+
type: text-generation
|
1532 |
+
name: text generation
|
1533 |
+
dataset:
|
1534 |
+
name: wic
|
1535 |
+
type: wic
|
1536 |
+
metrics:
|
1537 |
+
- name: acc
|
1538 |
+
type: acc
|
1539 |
+
value: 0.5015673981191222
|
1540 |
+
verified: false
|
1541 |
+
- task:
|
1542 |
+
type: text-generation
|
1543 |
+
name: text generation
|
1544 |
+
dataset:
|
1545 |
+
name: winogrande
|
1546 |
+
type: winogrande
|
1547 |
+
metrics:
|
1548 |
+
- name: acc
|
1549 |
+
type: acc
|
1550 |
+
value: 0.5864246250986582
|
1551 |
+
verified: false
|
1552 |
+
- task:
|
1553 |
+
type: text-generation
|
1554 |
+
name: text generation
|
1555 |
+
dataset:
|
1556 |
+
name: wnli
|
1557 |
+
type: wnli
|
1558 |
+
metrics:
|
1559 |
+
- name: acc
|
1560 |
+
type: acc
|
1561 |
+
value: 0.471830985915493
|
1562 |
+
verified: false
|
1563 |
+
- task:
|
1564 |
+
type: text-generation
|
1565 |
+
name: text generation
|
1566 |
+
dataset:
|
1567 |
+
name: wsc
|
1568 |
+
type: wsc
|
1569 |
+
metrics:
|
1570 |
+
- name: acc
|
1571 |
+
type: acc
|
1572 |
+
value: 0.4423076923076923
|
1573 |
+
verified: false
|
1574 |
+
- task:
|
1575 |
+
type: text-generation
|
1576 |
+
name: text generation
|
1577 |
+
dataset:
|
1578 |
+
name: humaneval
|
1579 |
+
type: humaneval
|
1580 |
+
metrics:
|
1581 |
+
- name: pass@1
|
1582 |
+
type: pass@1
|
1583 |
+
value: 0.15524390243902436
|
1584 |
+
verified: false
|
1585 |
+
- name: pass@10
|
1586 |
+
type: pass@10
|
1587 |
+
value: 0.3220367632383857
|
1588 |
+
verified: false
|
1589 |
+
- name: pass@100
|
1590 |
+
type: pass@100
|
1591 |
+
value: 0.5545431515723145
|
1592 |
+
verified: false
|
1593 |
---
|
1594 |
|
1595 |
<h1 style='text-align: center '>BLOOM LM</h1>
|
|
|
1993 |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
|
1994 |
|
1995 |
### Factors
|
1996 |
+
*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*
|
1997 |
|
1998 |
- Language, such as English or Yoruba
|
1999 |
|
|
|
2004 |
### Results
|
2005 |
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
|
2006 |
|
2007 |
+
**Zero-shot evaluations:**
|
2008 |
+
|
2009 |
+
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
|
2010 |
+
|
2011 |
+
| Task | Language | Metric | BLOOM-2B5 |
|
2012 |
+
|:----|:----|:----|:----:|
|
2013 |
+
| arc_challenge | eng | acc ↑ | 0.28 |
|
2014 |
+
| arc_easy | eng | acc ↑ | 0.595 |
|
2015 |
+
| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
|
2016 |
+
| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
|
2017 |
+
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
|
2018 |
+
| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
|
2019 |
+
| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
|
2020 |
+
| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
|
2021 |
+
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
|
2022 |
+
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
|
2023 |
+
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
|
2024 |
+
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
|
2025 |
+
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
|
2026 |
+
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
|
2027 |
+
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
|
2028 |
+
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
|
2029 |
+
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
|
2030 |
+
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
|
2031 |
+
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
|
2032 |
+
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
|
2033 |
+
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
|
2034 |
+
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
|
2035 |
+
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
|
2036 |
+
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
|
2037 |
+
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
|
2038 |
+
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
|
2039 |
+
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
|
2040 |
+
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
|
2041 |
+
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
|
2042 |
+
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
|
2043 |
+
| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
|
2044 |
+
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
|
2045 |
+
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
|
2046 |
+
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
|
2047 |
+
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
|
2048 |
+
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
|
2049 |
+
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
|
2050 |
+
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
|
2051 |
+
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
|
2052 |
+
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
|
2053 |
+
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
|
2054 |
+
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
|
2055 |
+
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
|
2056 |
+
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
|
2057 |
+
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
|
2058 |
+
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
|
2059 |
+
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
|
2060 |
+
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
|
2061 |
+
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
|
2062 |
+
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
|
2063 |
+
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
|
2064 |
+
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
|
2065 |
+
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
|
2066 |
+
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
|
2067 |
+
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
|
2068 |
+
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
|
2069 |
+
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
|
2070 |
+
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
|
2071 |
+
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
|
2072 |
+
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
|
2073 |
+
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
|
2074 |
+
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
|
2075 |
+
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
|
2076 |
+
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
|
2077 |
+
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
|
2078 |
+
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
|
2079 |
+
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
|
2080 |
+
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
|
2081 |
+
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
|
2082 |
+
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
|
2083 |
+
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
|
2084 |
+
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
|
2085 |
+
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
|
2086 |
+
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
|
2087 |
+
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
|
2088 |
+
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
|
2089 |
+
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
|
2090 |
+
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
|
2091 |
+
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
|
2092 |
+
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
|
2093 |
+
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
|
2094 |
+
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
|
2095 |
+
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
|
2096 |
+
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
|
2097 |
+
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
|
2098 |
+
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
|
2099 |
+
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
|
2100 |
+
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
|
2101 |
+
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
|
2102 |
+
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
|
2103 |
+
| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
|
2104 |
+
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
|
2105 |
+
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
|
2106 |
+
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
|
2107 |
+
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
|
2108 |
+
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
|
2109 |
+
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
|
2110 |
+
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
|
2111 |
+
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
|
2112 |
+
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
|
2113 |
+
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
|
2114 |
+
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
|
2115 |
+
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
|
2116 |
+
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
|
2117 |
+
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
|
2118 |
+
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
|
2119 |
+
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
|
2120 |
+
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
|
2121 |
+
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
|
2122 |
+
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
|
2123 |
+
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
|
2124 |
+
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
|
2125 |
+
| headqa | esp | acc ↑ | 0.264 |
|
2126 |
+
| hellaswag | eng | acc ↑ | 0.412 |
|
2127 |
+
| logiqa | eng | acc ↑ | 0.207 |
|
2128 |
+
| mathqa | eng | acc ↑ | 0.25 |
|
2129 |
+
| mc_taco | eng | em ↑ | 0.119 |
|
2130 |
+
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
|
2131 |
+
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
|
2132 |
+
| mrpc | eng | acc ↑ | 0.586 |
|
2133 |
+
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
|
2134 |
+
| openbookqa | eng | acc ↑ | 0.216 |
|
2135 |
+
| piqa | eng | acc ↑ | 0.708 |
|
2136 |
+
| prost | eng | acc ↑ | 0.227 |
|
2137 |
+
| pubmedqa | eng | acc ↑ | 0.616 |
|
2138 |
+
| qnli | eng | acc ↑ | 0.507 |
|
2139 |
+
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
|
2140 |
+
| race | eng | acc ↑ | 0.352 |
|
2141 |
+
| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
|
2142 |
+
| sciq | eng | acc ↑ | 0.892 |
|
2143 |
+
| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
|
2144 |
+
| triviaqa | eng | acc ↑ | 0.042 |
|
2145 |
+
| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
|
2146 |
+
| webqs | eng | acc ↑ | 0.017 |
|
2147 |
+
| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
|
2148 |
+
| winogrande | eng | acc ↑ | 0.586 |
|
2149 |
+
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
|
2150 |
+
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
|
2151 |
+
| humaneval | python | pass@1 ↑ | 0.155 |
|
2152 |
+
| humaneval | python | pass@10 ↑ | 0.322 |
|
2153 |
+
| humaneval | python | pass@100 ↑ | 0.555 |
|
2154 |
+
|
2155 |
**Train-time Evaluation:**
|
2156 |
|
2157 |
As of 25.May.2022, 15:00 PST:
|
|
|
2162 |
|
2163 |
- Perplexity: 8.9
|
2164 |
|
|
|
|
|
2165 |
</details>
|
2166 |
<p> </p>
|
2167 |
|
|
|
2247 |
## Model Card Authors
|
2248 |
*Ordered roughly chronologically and by amount of time spent.*
|
2249 |
|
2250 |
+
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
2251 |
|