Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results
text-generation-inference
Inference Endpoints
ybelkada Muennighoff commited on
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Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>

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@@ -155,6 +155,1601 @@ widget:
155
  A: Let's think step by step.
156
  example_title: Mathematical reasoning
157
  group: English
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
158
  ---
159
 
160
  <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
@@ -559,28 +2154,182 @@ Includes:
559
  And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
560
 
561
  ## Factors
562
- *This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
563
 
564
  - Language, such as English or Yoruba
565
 
566
  - Domain, such as newswire or stories
567
-
568
  - Demographic characteristics, such as gender or nationality
569
 
570
  ## Results
571
  *Results are based on the [Factors](#factors) and [Metrics](#metrics).*
572
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
573
  **Train-time Evaluation:**
574
 
575
- As of 25.May.2022, 15:00 PST:
576
 
577
- - Training Loss: 2.0
578
 
579
- - Validation Loss: 2.2
580
 
581
- - Perplexity: 8.9
582
 
583
- (More evaluation scores forthcoming.)
584
 
585
  </details>
586
 
@@ -675,4 +2424,4 @@ Initial prompting experiments using interim checkpoints: https://huggingface.co/
675
  # Model Card Authors
676
  *Ordered roughly chronologically and by amount of time spent.*
677
 
678
- 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
 
155
  A: Let's think step by step.
156
  example_title: Mathematical reasoning
157
  group: English
158
+ model-index:
159
+ - name: bloom
160
+ results:
161
+ - task:
162
+ type: text-generation
163
+ name: text generation
164
+ dataset:
165
+ name: arc_challenge
166
+ type: arc_challenge
167
+ metrics:
168
+ - name: acc
169
+ type: acc
170
+ value: 0.4112627986348123
171
+ verified: false
172
+ - task:
173
+ type: text-generation
174
+ name: text generation
175
+ dataset:
176
+ name: arc_easy
177
+ type: arc_easy
178
+ metrics:
179
+ - name: acc
180
+ type: acc
181
+ value: 0.726010101010101
182
+ verified: false
183
+ - task:
184
+ type: text-generation
185
+ name: text generation
186
+ dataset:
187
+ name: axb
188
+ type: axb
189
+ metrics:
190
+ - name: acc
191
+ type: acc
192
+ value: 0.5751811594202898
193
+ verified: false
194
+ - task:
195
+ type: text-generation
196
+ name: text generation
197
+ dataset:
198
+ name: axg
199
+ type: axg
200
+ metrics:
201
+ - name: acc
202
+ type: acc
203
+ value: 0.5252808988764045
204
+ verified: false
205
+ - task:
206
+ type: text-generation
207
+ name: text generation
208
+ dataset:
209
+ name: boolq
210
+ type: boolq
211
+ metrics:
212
+ - name: acc
213
+ type: acc
214
+ value: 0.6345565749235474
215
+ verified: false
216
+ - task:
217
+ type: text-generation
218
+ name: text generation
219
+ dataset:
220
+ name: cb
221
+ type: cb
222
+ metrics:
223
+ - name: acc
224
+ type: acc
225
+ value: 0.3392857142857143
226
+ verified: false
227
+ - task:
228
+ type: text-generation
229
+ name: text generation
230
+ dataset:
231
+ name: cola
232
+ type: cola
233
+ metrics:
234
+ - name: acc
235
+ type: acc
236
+ value: 0.39022051773729627
237
+ verified: false
238
+ - task:
239
+ type: text-generation
240
+ name: text generation
241
+ dataset:
242
+ name: copa
243
+ type: copa
244
+ metrics:
245
+ - name: acc
246
+ type: acc
247
+ value: 0.56
248
+ verified: false
249
+ - task:
250
+ type: text-generation
251
+ name: text generation
252
+ dataset:
253
+ name: crows_pairs_english
254
+ type: crows_pairs_english
255
+ metrics:
256
+ - name: acc
257
+ type: acc
258
+ value: 0.5
259
+ verified: false
260
+ - task:
261
+ type: text-generation
262
+ name: text generation
263
+ dataset:
264
+ name: crows_pairs_french
265
+ type: crows_pairs_french
266
+ metrics:
267
+ - name: acc
268
+ type: acc
269
+ value: 0.505664877757901
270
+ verified: false
271
+ - task:
272
+ type: text-generation
273
+ name: text generation
274
+ dataset:
275
+ name: diabla
276
+ type: diabla
277
+ metrics:
278
+ - name: acc
279
+ type: acc
280
+ value: 0.2947981906750174
281
+ verified: false
282
+ - task:
283
+ type: text-generation
284
+ name: text generation
285
+ dataset:
286
+ name: gsarti/flores_101_afr
287
+ type: gsarti/flores_101_afr
288
+ metrics:
289
+ - name: byte_perplexity
290
+ type: byte_perplexity
291
+ value: 4.25431550058444
292
+ verified: false
293
+ - task:
294
+ type: text-generation
295
+ name: text generation
296
+ dataset:
297
+ name: gsarti/flores_101_amh
298
+ type: gsarti/flores_101_amh
299
+ metrics:
300
+ - name: byte_perplexity
301
+ type: byte_perplexity
302
+ value: 3.716877477347089
303
+ verified: false
304
+ - task:
305
+ type: text-generation
306
+ name: text generation
307
+ dataset:
308
+ name: gsarti/flores_101_ara
309
+ type: gsarti/flores_101_ara
310
+ metrics:
311
+ - name: byte_perplexity
312
+ type: byte_perplexity
313
+ value: 1.7049030137120964
314
+ verified: false
315
+ - task:
316
+ type: text-generation
317
+ name: text generation
318
+ dataset:
319
+ name: gsarti/flores_101_asm
320
+ type: gsarti/flores_101_asm
321
+ metrics:
322
+ - name: byte_perplexity
323
+ type: byte_perplexity
324
+ value: 6.576581380404954
325
+ verified: false
326
+ - task:
327
+ type: text-generation
328
+ name: text generation
329
+ dataset:
330
+ name: gsarti/flores_101_ast
331
+ type: gsarti/flores_101_ast
332
+ metrics:
333
+ - name: byte_perplexity
334
+ type: byte_perplexity
335
+ value: 2.8562364775797944
336
+ verified: false
337
+ - task:
338
+ type: text-generation
339
+ name: text generation
340
+ dataset:
341
+ name: gsarti/flores_101_azj
342
+ type: gsarti/flores_101_azj
343
+ metrics:
344
+ - name: byte_perplexity
345
+ type: byte_perplexity
346
+ value: 4.80721528624391
347
+ verified: false
348
+ - task:
349
+ type: text-generation
350
+ name: text generation
351
+ dataset:
352
+ name: gsarti/flores_101_bel
353
+ type: gsarti/flores_101_bel
354
+ metrics:
355
+ - name: byte_perplexity
356
+ type: byte_perplexity
357
+ value: 2.7312177406635065
358
+ verified: false
359
+ - task:
360
+ type: text-generation
361
+ name: text generation
362
+ dataset:
363
+ name: gsarti/flores_101_ben
364
+ type: gsarti/flores_101_ben
365
+ metrics:
366
+ - name: byte_perplexity
367
+ type: byte_perplexity
368
+ value: 5.993409478990023
369
+ verified: false
370
+ - task:
371
+ type: text-generation
372
+ name: text generation
373
+ dataset:
374
+ name: gsarti/flores_101_bos
375
+ type: gsarti/flores_101_bos
376
+ metrics:
377
+ - name: byte_perplexity
378
+ type: byte_perplexity
379
+ value: 3.5936169095529493
380
+ verified: false
381
+ - task:
382
+ type: text-generation
383
+ name: text generation
384
+ dataset:
385
+ name: gsarti/flores_101_bul
386
+ type: gsarti/flores_101_bul
387
+ metrics:
388
+ - name: byte_perplexity
389
+ type: byte_perplexity
390
+ value: 2.159035321398085
391
+ verified: false
392
+ - task:
393
+ type: text-generation
394
+ name: text generation
395
+ dataset:
396
+ name: gsarti/flores_101_cat
397
+ type: gsarti/flores_101_cat
398
+ metrics:
399
+ - name: byte_perplexity
400
+ type: byte_perplexity
401
+ value: 2.167873680006659
402
+ verified: false
403
+ - task:
404
+ type: text-generation
405
+ name: text generation
406
+ dataset:
407
+ name: gsarti/flores_101_ceb
408
+ type: gsarti/flores_101_ceb
409
+ metrics:
410
+ - name: byte_perplexity
411
+ type: byte_perplexity
412
+ value: 5.286975089885673
413
+ verified: false
414
+ - task:
415
+ type: text-generation
416
+ name: text generation
417
+ dataset:
418
+ name: gsarti/flores_101_ces
419
+ type: gsarti/flores_101_ces
420
+ metrics:
421
+ - name: byte_perplexity
422
+ type: byte_perplexity
423
+ value: 3.4516208322236017
424
+ verified: false
425
+ - task:
426
+ type: text-generation
427
+ name: text generation
428
+ dataset:
429
+ name: gsarti/flores_101_ckb
430
+ type: gsarti/flores_101_ckb
431
+ metrics:
432
+ - name: byte_perplexity
433
+ type: byte_perplexity
434
+ value: 3.7051034724765612
435
+ verified: false
436
+ - task:
437
+ type: text-generation
438
+ name: text generation
439
+ dataset:
440
+ name: gsarti/flores_101_cym
441
+ type: gsarti/flores_101_cym
442
+ metrics:
443
+ - name: byte_perplexity
444
+ type: byte_perplexity
445
+ value: 7.0889312398688125
446
+ verified: false
447
+ - task:
448
+ type: text-generation
449
+ name: text generation
450
+ dataset:
451
+ name: gsarti/flores_101_dan
452
+ type: gsarti/flores_101_dan
453
+ metrics:
454
+ - name: byte_perplexity
455
+ type: byte_perplexity
456
+ value: 3.4300748208111838
457
+ verified: false
458
+ - task:
459
+ type: text-generation
460
+ name: text generation
461
+ dataset:
462
+ name: gsarti/flores_101_deu
463
+ type: gsarti/flores_101_deu
464
+ metrics:
465
+ - name: byte_perplexity
466
+ type: byte_perplexity
467
+ value: 2.3380585896268107
468
+ verified: false
469
+ - task:
470
+ type: text-generation
471
+ name: text generation
472
+ dataset:
473
+ name: gsarti/flores_101_ell
474
+ type: gsarti/flores_101_ell
475
+ metrics:
476
+ - name: byte_perplexity
477
+ type: byte_perplexity
478
+ value: 1.9595604725375586
479
+ verified: false
480
+ - task:
481
+ type: text-generation
482
+ name: text generation
483
+ dataset:
484
+ name: gsarti/flores_101_eng
485
+ type: gsarti/flores_101_eng
486
+ metrics:
487
+ - name: byte_perplexity
488
+ type: byte_perplexity
489
+ value: 1.8819637649637901
490
+ verified: false
491
+ - task:
492
+ type: text-generation
493
+ name: text generation
494
+ dataset:
495
+ name: gsarti/flores_101_est
496
+ type: gsarti/flores_101_est
497
+ metrics:
498
+ - name: byte_perplexity
499
+ type: byte_perplexity
500
+ value: 5.773850600380297
501
+ verified: false
502
+ - task:
503
+ type: text-generation
504
+ name: text generation
505
+ dataset:
506
+ name: gsarti/flores_101_fas
507
+ type: gsarti/flores_101_fas
508
+ metrics:
509
+ - name: byte_perplexity
510
+ type: byte_perplexity
511
+ value: 2.4306140728294086
512
+ verified: false
513
+ - task:
514
+ type: text-generation
515
+ name: text generation
516
+ dataset:
517
+ name: gsarti/flores_101_fin
518
+ type: gsarti/flores_101_fin
519
+ metrics:
520
+ - name: byte_perplexity
521
+ type: byte_perplexity
522
+ value: 4.304305536244342
523
+ verified: false
524
+ - task:
525
+ type: text-generation
526
+ name: text generation
527
+ dataset:
528
+ name: gsarti/flores_101_fra
529
+ type: gsarti/flores_101_fra
530
+ metrics:
531
+ - name: byte_perplexity
532
+ type: byte_perplexity
533
+ value: 1.9374688438541796
534
+ verified: false
535
+ - task:
536
+ type: text-generation
537
+ name: text generation
538
+ dataset:
539
+ name: gsarti/flores_101_ful
540
+ type: gsarti/flores_101_ful
541
+ metrics:
542
+ - name: byte_perplexity
543
+ type: byte_perplexity
544
+ value: 9.740353097219378
545
+ verified: false
546
+ - task:
547
+ type: text-generation
548
+ name: text generation
549
+ dataset:
550
+ name: gsarti/flores_101_gle
551
+ type: gsarti/flores_101_gle
552
+ metrics:
553
+ - name: byte_perplexity
554
+ type: byte_perplexity
555
+ value: 6.035269765075012
556
+ verified: false
557
+ - task:
558
+ type: text-generation
559
+ name: text generation
560
+ dataset:
561
+ name: gsarti/flores_101_glg
562
+ type: gsarti/flores_101_glg
563
+ metrics:
564
+ - name: byte_perplexity
565
+ type: byte_perplexity
566
+ value: 2.365451129546636
567
+ verified: false
568
+ - task:
569
+ type: text-generation
570
+ name: text generation
571
+ dataset:
572
+ name: gsarti/flores_101_guj
573
+ type: gsarti/flores_101_guj
574
+ metrics:
575
+ - name: byte_perplexity
576
+ type: byte_perplexity
577
+ value: 5.70676742569154
578
+ verified: false
579
+ - task:
580
+ type: text-generation
581
+ name: text generation
582
+ dataset:
583
+ name: gsarti/flores_101_hau
584
+ type: gsarti/flores_101_hau
585
+ metrics:
586
+ - name: byte_perplexity
587
+ type: byte_perplexity
588
+ value: 8.855204288260023
589
+ verified: false
590
+ - task:
591
+ type: text-generation
592
+ name: text generation
593
+ dataset:
594
+ name: gsarti/flores_101_heb
595
+ type: gsarti/flores_101_heb
596
+ metrics:
597
+ - name: byte_perplexity
598
+ type: byte_perplexity
599
+ value: 2.920943798471208
600
+ verified: false
601
+ - task:
602
+ type: text-generation
603
+ name: text generation
604
+ dataset:
605
+ name: gsarti/flores_101_hin
606
+ type: gsarti/flores_101_hin
607
+ metrics:
608
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+ - name: byte_perplexity
1236
+ type: byte_perplexity
1237
+ value: 6.8098996634099445
1238
+ verified: false
1239
+ - task:
1240
+ type: text-generation
1241
+ name: text generation
1242
+ dataset:
1243
+ name: gsarti/flores_101_tgk
1244
+ type: gsarti/flores_101_tgk
1245
+ metrics:
1246
+ - name: byte_perplexity
1247
+ type: byte_perplexity
1248
+ value: 3.785457016715163
1249
+ verified: false
1250
+ - task:
1251
+ type: text-generation
1252
+ name: text generation
1253
+ dataset:
1254
+ name: gsarti/flores_101_tgl
1255
+ type: gsarti/flores_101_tgl
1256
+ metrics:
1257
+ - name: byte_perplexity
1258
+ type: byte_perplexity
1259
+ value: 3.7498953645610875
1260
+ verified: false
1261
+ - task:
1262
+ type: text-generation
1263
+ name: text generation
1264
+ dataset:
1265
+ name: gsarti/flores_101_tha
1266
+ type: gsarti/flores_101_tha
1267
+ metrics:
1268
+ - name: byte_perplexity
1269
+ type: byte_perplexity
1270
+ value: 2.104151663233468
1271
+ verified: false
1272
+ - task:
1273
+ type: text-generation
1274
+ name: text generation
1275
+ dataset:
1276
+ name: gsarti/flores_101_tur
1277
+ type: gsarti/flores_101_tur
1278
+ metrics:
1279
+ - name: byte_perplexity
1280
+ type: byte_perplexity
1281
+ value: 3.3178240103796037
1282
+ verified: false
1283
+ - task:
1284
+ type: text-generation
1285
+ name: text generation
1286
+ dataset:
1287
+ name: gsarti/flores_101_ukr
1288
+ type: gsarti/flores_101_ukr
1289
+ metrics:
1290
+ - name: byte_perplexity
1291
+ type: byte_perplexity
1292
+ value: 2.088543437159643
1293
+ verified: false
1294
+ - task:
1295
+ type: text-generation
1296
+ name: text generation
1297
+ dataset:
1298
+ name: gsarti/flores_101_umb
1299
+ type: gsarti/flores_101_umb
1300
+ metrics:
1301
+ - name: byte_perplexity
1302
+ type: byte_perplexity
1303
+ value: 11.766013385445124
1304
+ verified: false
1305
+ - task:
1306
+ type: text-generation
1307
+ name: text generation
1308
+ dataset:
1309
+ name: gsarti/flores_101_urd
1310
+ type: gsarti/flores_101_urd
1311
+ metrics:
1312
+ - name: byte_perplexity
1313
+ type: byte_perplexity
1314
+ value: 1.7788699847612357
1315
+ verified: false
1316
+ - task:
1317
+ type: text-generation
1318
+ name: text generation
1319
+ dataset:
1320
+ name: gsarti/flores_101_uzb
1321
+ type: gsarti/flores_101_uzb
1322
+ metrics:
1323
+ - name: byte_perplexity
1324
+ type: byte_perplexity
1325
+ value: 8.499879863290486
1326
+ verified: false
1327
+ - task:
1328
+ type: text-generation
1329
+ name: text generation
1330
+ dataset:
1331
+ name: gsarti/flores_101_vie
1332
+ type: gsarti/flores_101_vie
1333
+ metrics:
1334
+ - name: byte_perplexity
1335
+ type: byte_perplexity
1336
+ value: 1.65901207387262
1337
+ verified: false
1338
+ - task:
1339
+ type: text-generation
1340
+ name: text generation
1341
+ dataset:
1342
+ name: gsarti/flores_101_wol
1343
+ type: gsarti/flores_101_wol
1344
+ metrics:
1345
+ - name: byte_perplexity
1346
+ type: byte_perplexity
1347
+ value: 6.141703791276928
1348
+ verified: false
1349
+ - task:
1350
+ type: text-generation
1351
+ name: text generation
1352
+ dataset:
1353
+ name: gsarti/flores_101_xho
1354
+ type: gsarti/flores_101_xho
1355
+ metrics:
1356
+ - name: byte_perplexity
1357
+ type: byte_perplexity
1358
+ value: 4.690199677955254
1359
+ verified: false
1360
+ - task:
1361
+ type: text-generation
1362
+ name: text generation
1363
+ dataset:
1364
+ name: gsarti/flores_101_yor
1365
+ type: gsarti/flores_101_yor
1366
+ metrics:
1367
+ - name: byte_perplexity
1368
+ type: byte_perplexity
1369
+ value: 4.360585696242932
1370
+ verified: false
1371
+ - task:
1372
+ type: text-generation
1373
+ name: text generation
1374
+ dataset:
1375
+ name: gsarti/flores_101_zho_simpl
1376
+ type: gsarti/flores_101_zho_simpl
1377
+ metrics:
1378
+ - name: byte_perplexity
1379
+ type: byte_perplexity
1380
+ value: 2.1183545781883515
1381
+ verified: false
1382
+ - task:
1383
+ type: text-generation
1384
+ name: text generation
1385
+ dataset:
1386
+ name: gsarti/flores_101_zho_trad
1387
+ type: gsarti/flores_101_zho_trad
1388
+ metrics:
1389
+ - name: byte_perplexity
1390
+ type: byte_perplexity
1391
+ value: 2.273787884962656
1392
+ verified: false
1393
+ - task:
1394
+ type: text-generation
1395
+ name: text generation
1396
+ dataset:
1397
+ name: gsarti/flores_101_zul
1398
+ type: gsarti/flores_101_zul
1399
+ metrics:
1400
+ - name: byte_perplexity
1401
+ type: byte_perplexity
1402
+ value: 6.016954767729589
1403
+ verified: false
1404
+ - task:
1405
+ type: text-generation
1406
+ name: text generation
1407
+ dataset:
1408
+ name: headqa
1409
+ type: headqa
1410
+ metrics:
1411
+ - name: acc
1412
+ type: acc
1413
+ value: 0.3464624361779723
1414
+ verified: false
1415
+ - task:
1416
+ type: text-generation
1417
+ name: text generation
1418
+ dataset:
1419
+ name: hellaswag
1420
+ type: hellaswag
1421
+ metrics:
1422
+ - name: acc
1423
+ type: acc
1424
+ value: 0.5353515236008763
1425
+ verified: false
1426
+ - task:
1427
+ type: text-generation
1428
+ name: text generation
1429
+ dataset:
1430
+ name: lambada_mt_de
1431
+ type: lambada_mt_de
1432
+ metrics:
1433
+ - name: acc
1434
+ type: acc
1435
+ value: 0.3291286629148069
1436
+ verified: false
1437
+ - task:
1438
+ type: text-generation
1439
+ name: text generation
1440
+ dataset:
1441
+ name: lambada_mt_en
1442
+ type: lambada_mt_en
1443
+ metrics:
1444
+ - name: acc
1445
+ type: acc
1446
+ value: 0.6720357073549389
1447
+ verified: false
1448
+ - task:
1449
+ type: text-generation
1450
+ name: text generation
1451
+ dataset:
1452
+ name: lambada_mt_es
1453
+ type: lambada_mt_es
1454
+ metrics:
1455
+ - name: acc
1456
+ type: acc
1457
+ value: 0.476421502037648
1458
+ verified: false
1459
+ - task:
1460
+ type: text-generation
1461
+ name: text generation
1462
+ dataset:
1463
+ name: lambada_mt_it
1464
+ type: lambada_mt_it
1465
+ metrics:
1466
+ - name: acc
1467
+ type: acc
1468
+ value: 0.4061711624296526
1469
+ verified: false
1470
+ - task:
1471
+ type: text-generation
1472
+ name: text generation
1473
+ dataset:
1474
+ name: logiqa
1475
+ type: logiqa
1476
+ metrics:
1477
+ - name: acc
1478
+ type: acc
1479
+ value: 0.2350230414746544
1480
+ verified: false
1481
+ - task:
1482
+ type: text-generation
1483
+ name: text generation
1484
+ dataset:
1485
+ name: mathqa
1486
+ type: mathqa
1487
+ metrics:
1488
+ - name: acc
1489
+ type: acc
1490
+ value: 0.27671691792294806
1491
+ verified: false
1492
+ - task:
1493
+ type: text-generation
1494
+ name: text generation
1495
+ dataset:
1496
+ name: mc_taco
1497
+ type: mc_taco
1498
+ metrics:
1499
+ - name: em
1500
+ type: em
1501
+ value: 0.13063063063063063
1502
+ verified: false
1503
+ - task:
1504
+ type: text-generation
1505
+ name: text generation
1506
+ dataset:
1507
+ name: mnli
1508
+ type: mnli
1509
+ metrics:
1510
+ - name: acc
1511
+ type: acc
1512
+ value: 0.3545565500406835
1513
+ verified: false
1514
+ - task:
1515
+ type: text-generation
1516
+ name: text generation
1517
+ dataset:
1518
+ name: mnli_mismatched
1519
+ type: mnli_mismatched
1520
+ metrics:
1521
+ - name: acc
1522
+ type: acc
1523
+ value: 0.3545565500406835
1524
+ verified: false
1525
+ - task:
1526
+ type: text-generation
1527
+ name: text generation
1528
+ dataset:
1529
+ name: mrpc
1530
+ type: mrpc
1531
+ metrics:
1532
+ - name: acc
1533
+ type: acc
1534
+ value: 0.3872549019607843
1535
+ verified: false
1536
+ - task:
1537
+ type: text-generation
1538
+ name: text generation
1539
+ dataset:
1540
+ name: multirc
1541
+ type: multirc
1542
+ metrics:
1543
+ - name: acc
1544
+ type: acc
1545
+ value: 0.570957095709571
1546
+ verified: false
1547
+ - task:
1548
+ type: text-generation
1549
+ name: text generation
1550
+ dataset:
1551
+ name: openbookqa
1552
+ type: openbookqa
1553
+ metrics:
1554
+ - name: acc
1555
+ type: acc
1556
+ value: 0.312
1557
+ verified: false
1558
+ - task:
1559
+ type: text-generation
1560
+ name: text generation
1561
+ dataset:
1562
+ name: piqa
1563
+ type: piqa
1564
+ metrics:
1565
+ - name: acc
1566
+ type: acc
1567
+ value: 0.7812840043525572
1568
+ verified: false
1569
+ - task:
1570
+ type: text-generation
1571
+ name: text generation
1572
+ dataset:
1573
+ name: prost
1574
+ type: prost
1575
+ metrics:
1576
+ - name: acc
1577
+ type: acc
1578
+ value: 0.2977156276686593
1579
+ verified: false
1580
+ - task:
1581
+ type: text-generation
1582
+ name: text generation
1583
+ dataset:
1584
+ name: pubmedqa
1585
+ type: pubmedqa
1586
+ metrics:
1587
+ - name: acc
1588
+ type: acc
1589
+ value: 0.741
1590
+ verified: false
1591
+ - task:
1592
+ type: text-generation
1593
+ name: text generation
1594
+ dataset:
1595
+ name: qnli
1596
+ type: qnli
1597
+ metrics:
1598
+ - name: acc
1599
+ type: acc
1600
+ value: 0.5172981878088962
1601
+ verified: false
1602
+ - task:
1603
+ type: text-generation
1604
+ name: text generation
1605
+ dataset:
1606
+ name: qqp
1607
+ type: qqp
1608
+ metrics:
1609
+ - name: acc
1610
+ type: acc
1611
+ value: 0.5883007667573584
1612
+ verified: false
1613
+ - task:
1614
+ type: text-generation
1615
+ name: text generation
1616
+ dataset:
1617
+ name: race
1618
+ type: race
1619
+ metrics:
1620
+ - name: acc
1621
+ type: acc
1622
+ value: 0.39043062200956935
1623
+ verified: false
1624
+ - task:
1625
+ type: text-generation
1626
+ name: text generation
1627
+ dataset:
1628
+ name: rte
1629
+ type: rte
1630
+ metrics:
1631
+ - name: acc
1632
+ type: acc
1633
+ value: 0.5198555956678701
1634
+ verified: false
1635
+ - task:
1636
+ type: text-generation
1637
+ name: text generation
1638
+ dataset:
1639
+ name: sciq
1640
+ type: sciq
1641
+ metrics:
1642
+ - name: acc
1643
+ type: acc
1644
+ value: 0.936
1645
+ verified: false
1646
+ - task:
1647
+ type: text-generation
1648
+ name: text generation
1649
+ dataset:
1650
+ name: sst
1651
+ type: sst
1652
+ metrics:
1653
+ - name: acc
1654
+ type: acc
1655
+ value: 0.6043577981651376
1656
+ verified: false
1657
+ - task:
1658
+ type: text-generation
1659
+ name: text generation
1660
+ dataset:
1661
+ name: triviaqa
1662
+ type: triviaqa
1663
+ metrics:
1664
+ - name: acc
1665
+ type: acc
1666
+ value: 0.18332891363917617
1667
+ verified: false
1668
+ - task:
1669
+ type: text-generation
1670
+ name: text generation
1671
+ dataset:
1672
+ name: tydiqa_primary
1673
+ type: tydiqa_primary
1674
+ metrics:
1675
+ - name: acc
1676
+ type: acc
1677
+ value: 0.2809817301342725
1678
+ verified: false
1679
+ - task:
1680
+ type: text-generation
1681
+ name: text generation
1682
+ dataset:
1683
+ name: webqs
1684
+ type: webqs
1685
+ metrics:
1686
+ - name: acc
1687
+ type: acc
1688
+ value: 0.061515748031496065
1689
+ verified: false
1690
+ - task:
1691
+ type: text-generation
1692
+ name: text generation
1693
+ dataset:
1694
+ name: wic
1695
+ type: wic
1696
+ metrics:
1697
+ - name: acc
1698
+ type: acc
1699
+ value: 0.5062695924764891
1700
+ verified: false
1701
+ - task:
1702
+ type: text-generation
1703
+ name: text generation
1704
+ dataset:
1705
+ name: winogrande
1706
+ type: winogrande
1707
+ metrics:
1708
+ - name: acc
1709
+ type: acc
1710
+ value: 0.7095501183898973
1711
+ verified: false
1712
+ - task:
1713
+ type: text-generation
1714
+ name: text generation
1715
+ dataset:
1716
+ name: wnli
1717
+ type: wnli
1718
+ metrics:
1719
+ - name: acc
1720
+ type: acc
1721
+ value: 0.5704225352112676
1722
+ verified: false
1723
+ - task:
1724
+ type: text-generation
1725
+ name: text generation
1726
+ dataset:
1727
+ name: wsc
1728
+ type: wsc
1729
+ metrics:
1730
+ - name: acc
1731
+ type: acc
1732
+ value: 0.5192307692307693
1733
+ verified: false
1734
+ - task:
1735
+ type: text-generation
1736
+ name: text generation
1737
+ dataset:
1738
+ name: humaneval
1739
+ type: humaneval
1740
+ metrics:
1741
+ - name: pass@1
1742
+ type: pass@1
1743
+ value: 0.15524390243902436
1744
+ verified: false
1745
+ - name: pass@10
1746
+ type: pass@10
1747
+ value: 0.3220367632383857
1748
+ verified: false
1749
+ - name: pass@100
1750
+ type: pass@100
1751
+ value: 0.5545431515723145
1752
+ verified: false
1753
  ---
1754
 
1755
  <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
2154
  And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
2155
 
2156
  ## Factors
2157
+ *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.*
2158
 
2159
  - Language, such as English or Yoruba
2160
 
2161
  - Domain, such as newswire or stories
2162
+
2163
  - Demographic characteristics, such as gender or nationality
2164
 
2165
  ## Results
2166
  *Results are based on the [Factors](#factors) and [Metrics](#metrics).*
2167
 
2168
+ **Zero-shot evaluations:**
2169
+
2170
+ See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
2171
+
2172
+ | Task | Language | Metric | BLOOM-176B | OPT-175B* |
2173
+ |:--------|:-----------------|:------------------------|-------------:|------------:|
2174
+ | arc_challenge | eng | acc ↑ | 0.411 | 0.412 |
2175
+ | arc_easy | eng | acc ↑ | 0.726 | 0.751 |
2176
+ | axb (Median of 10 prompts) | eng | acc ↑ | 0.575 | 0.532 |
2177
+ | axg (Median of 10 prompts) | eng | acc ↑ | 0.525 | 0.548 |
2178
+ | boolq (Median of 11 prompts) | eng | acc ↑ | 0.635 | 0.622 |
2179
+ | cb (Median of 15 prompts) | eng | acc ↑ | 0.339 | 0.411 |
2180
+ | cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
2181
+ | copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
2182
+ | crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
2183
+ | crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
2184
+ | diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
2185
+ | gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
2186
+ | gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
2187
+ | gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.705 | 2.42 |
2188
+ | gsarti/flores_101_asm | asm | byte_perplexity ↓ | 6.577 | 3.028 |
2189
+ | gsarti/flores_101_ast | ast | byte_perplexity ↓ | 2.856 | 4.737 |
2190
+ | gsarti/flores_101_azj | azj | byte_perplexity ↓ | 4.807 | 4.767 |
2191
+ | gsarti/flores_101_bel | bel | byte_perplexity ↓ | 2.731 | 2.557 |
2192
+ | gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.993 | 2.243 |
2193
+ | gsarti/flores_101_bos | bos | byte_perplexity ↓ | 3.594 | 2.668 |
2194
+ | gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.159 | 2.099 |
2195
+ | gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.168 | 2.837 |
2196
+ | gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 5.287 | 3.636 |
2197
+ | gsarti/flores_101_ces | ces | byte_perplexity ↓ | 3.452 | 2.749 |
2198
+ | gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.705 | 4.688 |
2199
+ | gsarti/flores_101_cym | cym | byte_perplexity ↓ | 7.089 | 5.075 |
2200
+ | gsarti/flores_101_dan | dan | byte_perplexity ↓ | 3.43 | 2.492 |
2201
+ | gsarti/flores_101_deu | deu | byte_perplexity ↓ | 2.338 | 2.099 |
2202
+ | gsarti/flores_101_ell | ell | byte_perplexity ↓ | 1.96 | 1.811 |
2203
+ | gsarti/flores_101_eng | eng | byte_perplexity ↓ | 1.882 | 1.9 |
2204
+ | gsarti/flores_101_est | est | byte_perplexity ↓ | 5.774 | 3.533 |
2205
+ | gsarti/flores_101_fas | fas | byte_perplexity ↓ | 2.431 | 2.444 |
2206
+ | gsarti/flores_101_fin | fin | byte_perplexity ↓ | 4.304 | 2.601 |
2207
+ | gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.937 | 1.984 |
2208
+ | gsarti/flores_101_ful | ful | byte_perplexity ↓ | 9.74 | 11.84 |
2209
+ | gsarti/flores_101_gle | gle | byte_perplexity ↓ | 6.035 | 3.914 |
2210
+ | gsarti/flores_101_glg | glg | byte_perplexity ↓ | 2.365 | 3.015 |
2211
+ | gsarti/flores_101_guj | guj | byte_perplexity ↓ | 5.707 | 2.438 |
2212
+ | gsarti/flores_101_hau | hau | byte_perplexity ↓ | 8.855 | 5.283 |
2213
+ | gsarti/flores_101_heb | heb | byte_perplexity ↓ | 2.921 | 2.903 |
2214
+ | gsarti/flores_101_hin | hin | byte_perplexity ↓ | 5.452 | 1.86 |
2215
+ | gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 3.706 | 2.715 |
2216
+ | gsarti/flores_101_hun | hun | byte_perplexity ↓ | 4.059 | 2.865 |
2217
+ | gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.127 | 3.411 |
2218
+ | gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 3.95 | 8.008 |
2219
+ | gsarti/flores_101_ind | ind | byte_perplexity ↓ | 1.976 | 2.632 |
2220
+ | gsarti/flores_101_isl | isl | byte_perplexity ↓ | 5.501 | 4.701 |
2221
+ | gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.314 | 2.104 |
2222
+ | gsarti/flores_101_jav | jav | byte_perplexity ↓ | 4.942 | 8.16 |
2223
+ | gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.259 | 2.198 |
2224
+ | gsarti/flores_101_kam | kam | byte_perplexity ↓ | 9.743 | 10.981 |
2225
+ | gsarti/flores_101_kan | kan | byte_perplexity ↓ | 6.234 | 2.373 |
2226
+ | gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.051 | 2.466 |
2227
+ | gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.039 | 4.376 |
2228
+ | gsarti/flores_101_kea | kea | byte_perplexity ↓ | 7.147 | 9.632 |
2229
+ | gsarti/flores_101_khm | khm | byte_perplexity ↓ | 3.367 | 2.646 |
2230
+ | gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.241 | 4.522 |
2231
+ | gsarti/flores_101_kor | kor | byte_perplexity ↓ | 2.902 | 3.376 |
2232
+ | gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.331 | 3.106 |
2233
+ | gsarti/flores_101_lav | lav | byte_perplexity ↓ | 5.224 | 4.811 |
2234
+ | gsarti/flores_101_lin | lin | byte_perplexity ↓ | 4.847 | 8.871 |
2235
+ | gsarti/flores_101_lit | lit | byte_perplexity ↓ | 4.543 | 5.183 |
2236
+ | gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 5.591 | 7.158 |
2237
+ | gsarti/flores_101_lug | lug | byte_perplexity ↓ | 5.43 | 7.399 |
2238
+ | gsarti/flores_101_luo | luo | byte_perplexity ↓ | 12.031 | 11.951 |
2239
+ | gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.794 | 2.054 |
2240
+ | gsarti/flores_101_mar | mar | byte_perplexity ↓ | 6.857 | 2.274 |
2241
+ | gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.335 | 2.538 |
2242
+ | gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 9.041 | 5.996 |
2243
+ | gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.095 | 4.519 |
2244
+ | gsarti/flores_101_mri | mri | byte_perplexity ↓ | 5.266 | 4.438 |
2245
+ | gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.222 | 2.935 |
2246
+ | gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.523 | 2.413 |
2247
+ | gsarti/flores_101_nld | nld | byte_perplexity ↓ | 2.799 | 2.293 |
2248
+ | gsarti/flores_101_nob | nob | byte_perplexity ↓ | 3.629 | 2.593 |
2249
+ | gsarti/flores_101_npi | npi | byte_perplexity ↓ | 6.666 | 2.499 |
2250
+ | gsarti/flores_101_nso | nso | byte_perplexity ↓ | 5.015 | 8.485 |
2251
+ | gsarti/flores_101_nya | nya | byte_perplexity ↓ | 4.938 | 7.548 |
2252
+ | gsarti/flores_101_oci | oci | byte_perplexity ↓ | 3.607 | 4.936 |
2253
+ | gsarti/flores_101_orm | orm | byte_perplexity ↓ | 11.316 | 7.145 |
2254
+ | gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.982 | 2.668 |
2255
+ | gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.772 | 2.782 |
2256
+ | gsarti/flores_101_pol | pol | byte_perplexity ↓ | 3.012 | 2.432 |
2257
+ | gsarti/flores_101_por | por | byte_perplexity ↓ | 1.841 | 2.178 |
2258
+ | gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.624 | 4.785 |
2259
+ | gsarti/flores_101_ron | ron | byte_perplexity ↓ | 3.05 | 2.197 |
2260
+ | gsarti/flores_101_rus | rus | byte_perplexity ↓ | 1.708 | 1.689 |
2261
+ | gsarti/flores_101_slk | slk | byte_perplexity ↓ | 4.038 | 3.419 |
2262
+ | gsarti/flores_101_slv | slv | byte_perplexity ↓ | 4.141 | 3.582 |
2263
+ | gsarti/flores_101_sna | sna | byte_perplexity ↓ | 4.711 | 5.588 |
2264
+ | gsarti/flores_101_snd | snd | byte_perplexity ↓ | 4.206 | 5.667 |
2265
+ | gsarti/flores_101_som | som | byte_perplexity ↓ | 9.154 | 4.788 |
2266
+ | gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.796 | 2.098 |
2267
+ | gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.241 | 2.688 |
2268
+ | gsarti/flores_101_swe | swe | byte_perplexity ↓ | 3.345 | 2.468 |
2269
+ | gsarti/flores_101_swh | swh | byte_perplexity ↓ | 2.684 | 4.473 |
2270
+ | gsarti/flores_101_tam | tam | byte_perplexity ↓ | 5.165 | 2.024 |
2271
+ | gsarti/flores_101_tel | tel | byte_perplexity ↓ | 6.81 | 2.407 |
2272
+ | gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.785 | 4.899 |
2273
+ | gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 3.75 | 2.738 |
2274
+ | gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.104 | 2.035 |
2275
+ | gsarti/flores_101_tur | tur | byte_perplexity ↓ | 3.318 | 2.622 |
2276
+ | gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.089 | 1.93 |
2277
+ | gsarti/flores_101_umb | umb | byte_perplexity ↓ | 11.766 | 11.64 |
2278
+ | gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.779 | 2.982 |
2279
+ | gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 8.5 | 13.209 |
2280
+ | gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.659 | 2.229 |
2281
+ | gsarti/flores_101_wol | wol | byte_perplexity ↓ | 6.142 | 13.945 |
2282
+ | gsarti/flores_101_xho | xho | byte_perplexity ↓ | 4.69 | 8.42 |
2283
+ | gsarti/flores_101_yor | yor | byte_perplexity ↓ | 4.361 | 7.636 |
2284
+ | gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.118 | 5.113 |
2285
+ | gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.274 | 5.67 |
2286
+ | gsarti/flores_101_zul | zul | byte_perplexity ↓ | 6.017 | 7.341 |
2287
+ | headqa | esp | acc ↑ | 0.346 | 0.244 |
2288
+ | hellaswag | eng | acc ↑ | 0.535 | 0.592 |
2289
+ | lambada_mt_de | deu | acc ↑ | 0.329 | 0.358 |
2290
+ | lambada_mt_en | eng | acc ↑ | 0.672 | 0.747 |
2291
+ | lambada_mt_es | esp | acc ↑ | 0.476 | 0.397 |
2292
+ | lambada_mt_it | ita | acc ↑ | 0.406 | 0.409 |
2293
+ | logiqa | eng | acc ↑ | 0.235 | 0.244 |
2294
+ | mathqa | eng | acc ↑ | 0.277 | 0.268 |
2295
+ | mc_taco | eng | em ↑ | 0.131 | 0.124 |
2296
+ | mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2297
+ | mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2298
+ | mrpc | eng | acc ↑ | 0.387 | 0.446 |
2299
+ | multirc (Median of 11 prompts) | eng | acc ↑ | 0.571 | 0.599 |
2300
+ | openbookqa | eng | acc ↑ | 0.312 | 0.322 |
2301
+ | piqa | eng | acc ↑ | 0.781 | 0.791 |
2302
+ | prost | eng | acc ↑ | 0.298 | 0.299 |
2303
+ | pubmedqa | eng | acc ↑ | 0.741 | 0.709 |
2304
+ | qnli | eng | acc ↑ | 0.517 | 0.554 |
2305
+ | qqp (Median of 7 prompts) | eng | acc ↑ | 0.588 | 0.395 |
2306
+ | race | eng | acc ↑ | 0.39 | 0.402 |
2307
+ | rte (Median of 6 prompts) | eng | acc ↑ | 0.52 | 0.495 |
2308
+ | sciq | eng | acc ↑ | 0.936 | 0.948 |
2309
+ | sst (Median of 6 prompts) | eng | acc ↑ | 0.604 | 0.647 |
2310
+ | triviaqa | eng | acc ↑ | 0.183 | 0.342 |
2311
+ | tydiqa_primary (Median of 16 prompts) | eng | acc ↑ | 0.281 | 0.148 |
2312
+ | webqs | eng | acc ↑ | 0.062 | 0.159 |
2313
+ | wic (Median of 11 prompts) | eng | acc ↑ | 0.506 | 0.498 |
2314
+ | winogrande | eng | acc ↑ | 0.71 | 0.736 |
2315
+ | wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
2316
+ | wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
2317
+ | humaneval | python | pass@1 | 0.155 | 0.0 |
2318
+ | humaneval | python | pass@10 | 0.322 | 0.0 |
2319
+ | humaneval | python | pass@100 | 0.555 | 0.003 |
2320
+
2321
+
2322
  **Train-time Evaluation:**
2323
 
2324
+ Final checkpoint after 95K steps:
2325
 
2326
+ - Training Loss: 1.939
2327
 
2328
+ - Validation Loss: 2.061
2329
 
2330
+ - Perplexity: 7.045
2331
 
2332
+ For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs
2333
 
2334
  </details>
2335
 
 
2424
  # Model Card Authors
2425
  *Ordered roughly chronologically and by amount of time spent.*
2426
 
2427
+ 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