Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results
text-generation-inference
Inference Endpoints
TimeRobber stellaathena commited on
Commit
3f4b529
1 Parent(s): aee8c40

Remove incorrect evaluation results. (#98)

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- Remove incorrect evaluation results. (50fcfe3722051a7eb7c649576a9fe0a65c20a319)


Co-authored-by: Stella Biderman <[email protected]>

Files changed (1) hide show
  1. README.md +2 -1719
README.md CHANGED
@@ -172,1579 +172,6 @@ widget:
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  model-index:
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  - name: bloom
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  results:
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- - task:
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- type: text-generation
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- name: text generation
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- dataset:
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- name: arc_challenge
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- type: arc_challenge
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- metrics:
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- type: acc
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- value: 0.4112627986348123
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- verified: false
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- type: text-generation
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- name: text generation
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- name: arc_easy
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- type: arc_easy
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- metrics:
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- type: acc
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- value: 0.726010101010101
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- verified: false
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- type: text-generation
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- name: text generation
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- type: axb
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- metrics:
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- - name: acc
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- type: acc
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- value: 0.5751811594202898
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- verified: false
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- type: text-generation
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- name: text generation
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- dataset:
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- name: axg
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- type: axg
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- metrics:
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- - name: acc
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- type: acc
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- value: 0.5252808988764045
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- verified: false
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- name: text generation
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- name: boolq
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- type: boolq
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- metrics:
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- type: acc
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- value: 0.6345565749235474
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- verified: false
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- name: text generation
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- name: cb
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- type: cb
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- metrics:
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- type: acc
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- value: 0.3392857142857143
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- verified: false
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- name: text generation
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- name: cola
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- type: cola
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- metrics:
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- type: acc
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- value: 0.39022051773729627
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- name: text generation
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- name: copa
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- type: copa
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- - name: acc
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- type: acc
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- value: 0.56
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- verified: false
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- type: text-generation
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- name: text generation
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- dataset:
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- name: crows_pairs_english
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- type: crows_pairs_english
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- metrics:
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- verified: false
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- name: text generation
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- name: crows_pairs_french
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- type: crows_pairs_french
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- verified: false
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- type: text-generation
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- name: text generation
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- name: diabla
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- type: diabla
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- metrics:
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- - name: acc
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- type: acc
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- value: 0.2947981906750174
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- verified: false
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- name: text generation
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- dataset:
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- name: gsarti/flores_101_afr
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- type: gsarti/flores_101_afr
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- metrics:
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- verified: false
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- name: text generation
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- type: gsarti/flores_101_amh
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1447
- - name: acc
1448
- type: acc
1449
- value: 0.3291286629148069
1450
- verified: false
1451
- - task:
1452
- type: text-generation
1453
- name: text generation
1454
- dataset:
1455
- name: lambada_mt_en
1456
- type: lambada_mt_en
1457
- metrics:
1458
- - name: acc
1459
- type: acc
1460
- value: 0.6720357073549389
1461
- verified: false
1462
- - task:
1463
- type: text-generation
1464
- name: text generation
1465
- dataset:
1466
- name: lambada_mt_es
1467
- type: lambada_mt_es
1468
- metrics:
1469
- - name: acc
1470
- type: acc
1471
- value: 0.476421502037648
1472
- verified: false
1473
- - task:
1474
- type: text-generation
1475
- name: text generation
1476
- dataset:
1477
- name: lambada_mt_it
1478
- type: lambada_mt_it
1479
- metrics:
1480
- - name: acc
1481
- type: acc
1482
- value: 0.4061711624296526
1483
- verified: false
1484
- - task:
1485
- type: text-generation
1486
- name: text generation
1487
- dataset:
1488
- name: logiqa
1489
- type: logiqa
1490
- metrics:
1491
- - name: acc
1492
- type: acc
1493
- value: 0.2350230414746544
1494
- verified: false
1495
- - task:
1496
- type: text-generation
1497
- name: text generation
1498
- dataset:
1499
- name: mathqa
1500
- type: mathqa
1501
- metrics:
1502
- - name: acc
1503
- type: acc
1504
- value: 0.27671691792294806
1505
- verified: false
1506
- - task:
1507
- type: text-generation
1508
- name: text generation
1509
- dataset:
1510
- name: mc_taco
1511
- type: mc_taco
1512
- metrics:
1513
- - name: em
1514
- type: em
1515
- value: 0.13063063063063063
1516
- verified: false
1517
- - task:
1518
- type: text-generation
1519
- name: text generation
1520
- dataset:
1521
- name: mnli
1522
- type: mnli
1523
- metrics:
1524
- - name: acc
1525
- type: acc
1526
- value: 0.3545565500406835
1527
- verified: false
1528
- - task:
1529
- type: text-generation
1530
- name: text generation
1531
- dataset:
1532
- name: mnli_mismatched
1533
- type: mnli_mismatched
1534
- metrics:
1535
- - name: acc
1536
- type: acc
1537
- value: 0.3545565500406835
1538
- verified: false
1539
- - task:
1540
- type: text-generation
1541
- name: text generation
1542
- dataset:
1543
- name: mrpc
1544
- type: mrpc
1545
- metrics:
1546
- - name: acc
1547
- type: acc
1548
- value: 0.3872549019607843
1549
- verified: false
1550
- - task:
1551
- type: text-generation
1552
- name: text generation
1553
- dataset:
1554
- name: multirc
1555
- type: multirc
1556
- metrics:
1557
- - name: acc
1558
- type: acc
1559
- value: 0.570957095709571
1560
- verified: false
1561
- - task:
1562
- type: text-generation
1563
- name: text generation
1564
- dataset:
1565
- name: openbookqa
1566
- type: openbookqa
1567
- metrics:
1568
- - name: acc
1569
- type: acc
1570
- value: 0.312
1571
- verified: false
1572
- - task:
1573
- type: text-generation
1574
- name: text generation
1575
- dataset:
1576
- name: piqa
1577
- type: piqa
1578
- metrics:
1579
- - name: acc
1580
- type: acc
1581
- value: 0.7812840043525572
1582
- verified: false
1583
- - task:
1584
- type: text-generation
1585
- name: text generation
1586
- dataset:
1587
- name: prost
1588
- type: prost
1589
- metrics:
1590
- - name: acc
1591
- type: acc
1592
- value: 0.2977156276686593
1593
- verified: false
1594
- - task:
1595
- type: text-generation
1596
- name: text generation
1597
- dataset:
1598
- name: pubmedqa
1599
- type: pubmedqa
1600
- metrics:
1601
- - name: acc
1602
- type: acc
1603
- value: 0.741
1604
- verified: false
1605
- - task:
1606
- type: text-generation
1607
- name: text generation
1608
- dataset:
1609
- name: qnli
1610
- type: qnli
1611
- metrics:
1612
- - name: acc
1613
- type: acc
1614
- value: 0.5172981878088962
1615
- verified: false
1616
- - task:
1617
- type: text-generation
1618
- name: text generation
1619
- dataset:
1620
- name: qqp
1621
- type: qqp
1622
- metrics:
1623
- - name: acc
1624
- type: acc
1625
- value: 0.5883007667573584
1626
- verified: false
1627
- - task:
1628
- type: text-generation
1629
- name: text generation
1630
- dataset:
1631
- name: race
1632
- type: race
1633
- metrics:
1634
- - name: acc
1635
- type: acc
1636
- value: 0.39043062200956935
1637
- verified: false
1638
- - task:
1639
- type: text-generation
1640
- name: text generation
1641
- dataset:
1642
- name: rte
1643
- type: rte
1644
- metrics:
1645
- - name: acc
1646
- type: acc
1647
- value: 0.5198555956678701
1648
- verified: false
1649
- - task:
1650
- type: text-generation
1651
- name: text generation
1652
- dataset:
1653
- name: sciq
1654
- type: sciq
1655
- metrics:
1656
- - name: acc
1657
- type: acc
1658
- value: 0.936
1659
- verified: false
1660
- - task:
1661
- type: text-generation
1662
- name: text generation
1663
- dataset:
1664
- name: sst
1665
- type: sst
1666
- metrics:
1667
- - name: acc
1668
- type: acc
1669
- value: 0.6043577981651376
1670
- verified: false
1671
- - task:
1672
- type: text-generation
1673
- name: text generation
1674
- dataset:
1675
- name: triviaqa
1676
- type: triviaqa
1677
- metrics:
1678
- - name: acc
1679
- type: acc
1680
- value: 0.18332891363917617
1681
- verified: false
1682
- - task:
1683
- type: text-generation
1684
- name: text generation
1685
- dataset:
1686
- name: tydiqa_primary
1687
- type: tydiqa_primary
1688
- metrics:
1689
- - name: acc
1690
- type: acc
1691
- value: 0.2809817301342725
1692
- verified: false
1693
- - task:
1694
- type: text-generation
1695
- name: text generation
1696
- dataset:
1697
- name: webqs
1698
- type: webqs
1699
- metrics:
1700
- - name: acc
1701
- type: acc
1702
- value: 0.061515748031496065
1703
- verified: false
1704
- - task:
1705
- type: text-generation
1706
- name: text generation
1707
- dataset:
1708
- name: wic
1709
- type: wic
1710
- metrics:
1711
- - name: acc
1712
- type: acc
1713
- value: 0.5062695924764891
1714
- verified: false
1715
- - task:
1716
- type: text-generation
1717
- name: text generation
1718
- dataset:
1719
- name: winogrande
1720
- type: winogrande
1721
- metrics:
1722
- - name: acc
1723
- type: acc
1724
- value: 0.7095501183898973
1725
- verified: false
1726
- - task:
1727
- type: text-generation
1728
- name: text generation
1729
- dataset:
1730
- name: wnli
1731
- type: wnli
1732
- metrics:
1733
- - name: acc
1734
- type: acc
1735
- value: 0.5704225352112676
1736
- verified: false
1737
- - task:
1738
- type: text-generation
1739
- name: text generation
1740
- dataset:
1741
- name: wsc
1742
- type: wsc
1743
- metrics:
1744
- - name: acc
1745
- type: acc
1746
- value: 0.5192307692307693
1747
- verified: false
1748
  - task:
1749
  type: text-generation
1750
  name: text generation
@@ -2164,7 +591,6 @@ Model may:
2164
  ## Metrics
2165
  *This section describes the different ways performance is calculated and why.*
2166
 
2167
-
2168
  Includes:
2169
 
2170
  | Metric | Why chosen |
@@ -2188,155 +614,12 @@ And multiple different metrics for specific tasks. _(More evaluation metrics for
2188
 
2189
  **Zero-shot evaluations:**
2190
 
2191
- # <span style="color:red"><b>WARNING:</b> These are <b>intermediate results</b></span>
2192
 
2193
  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
2194
 
2195
  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
2196
- |:--------|:-----------------|:------------------------|-------------:|------------:|
2197
- | arc_challenge | eng | acc ↑ | 0.411 | 0.412 |
2198
- | arc_easy | eng | acc ↑ | 0.726 | 0.751 |
2199
- | axb (Median of 10 prompts) | eng | acc ↑ | 0.575 | 0.532 |
2200
- | axg (Median of 10 prompts) | eng | acc ↑ | 0.525 | 0.548 |
2201
- | boolq (Median of 11 prompts) | eng | acc ↑ | 0.635 | 0.622 |
2202
- | cb (Median of 15 prompts) | eng | acc ↑ | 0.339 | 0.411 |
2203
- | cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
2204
- | copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
2205
- | crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
2206
- | crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
2207
- | diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
2208
- | gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
2209
- | gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
2210
- | gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.705 | 2.42 |
2211
- | gsarti/flores_101_asm | asm | byte_perplexity ↓ | 6.577 | 3.028 |
2212
- | gsarti/flores_101_ast | ast | byte_perplexity ↓ | 2.856 | 4.737 |
2213
- | gsarti/flores_101_azj | azj | byte_perplexity ↓ | 4.807 | 4.767 |
2214
- | gsarti/flores_101_bel | bel | byte_perplexity ↓ | 2.731 | 2.557 |
2215
- | gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.993 | 2.243 |
2216
- | gsarti/flores_101_bos | bos | byte_perplexity ↓ | 3.594 | 2.668 |
2217
- | gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.159 | 2.099 |
2218
- | gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.168 | 2.837 |
2219
- | gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 5.287 | 3.636 |
2220
- | gsarti/flores_101_ces | ces | byte_perplexity ↓ | 3.452 | 2.749 |
2221
- | gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.705 | 4.688 |
2222
- | gsarti/flores_101_cym | cym | byte_perplexity ↓ | 7.089 | 5.075 |
2223
- | gsarti/flores_101_dan | dan | byte_perplexity ↓ | 3.43 | 2.492 |
2224
- | gsarti/flores_101_deu | deu | byte_perplexity ↓ | 2.338 | 2.099 |
2225
- | gsarti/flores_101_ell | ell | byte_perplexity ↓ | 1.96 | 1.811 |
2226
- | gsarti/flores_101_eng | eng | byte_perplexity ↓ | 1.882 | 1.9 |
2227
- | gsarti/flores_101_est | est | byte_perplexity ↓ | 5.774 | 3.533 |
2228
- | gsarti/flores_101_fas | fas | byte_perplexity ↓ | 2.431 | 2.444 |
2229
- | gsarti/flores_101_fin | fin | byte_perplexity ↓ | 4.304 | 2.601 |
2230
- | gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.937 | 1.984 |
2231
- | gsarti/flores_101_ful | ful | byte_perplexity ↓ | 9.74 | 11.84 |
2232
- | gsarti/flores_101_gle | gle | byte_perplexity ↓ | 6.035 | 3.914 |
2233
- | gsarti/flores_101_glg | glg | byte_perplexity ↓ | 2.365 | 3.015 |
2234
- | gsarti/flores_101_guj | guj | byte_perplexity ↓ | 5.707 | 2.438 |
2235
- | gsarti/flores_101_hau | hau | byte_perplexity ↓ | 8.855 | 5.283 |
2236
- | gsarti/flores_101_heb | heb | byte_perplexity ↓ | 2.921 | 2.903 |
2237
- | gsarti/flores_101_hin | hin | byte_perplexity ↓ | 5.452 | 1.86 |
2238
- | gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 3.706 | 2.715 |
2239
- | gsarti/flores_101_hun | hun | byte_perplexity ↓ | 4.059 | 2.865 |
2240
- | gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.127 | 3.411 |
2241
- | gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 3.95 | 8.008 |
2242
- | gsarti/flores_101_ind | ind | byte_perplexity ↓ | 1.976 | 2.632 |
2243
- | gsarti/flores_101_isl | isl | byte_perplexity ↓ | 5.501 | 4.701 |
2244
- | gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.314 | 2.104 |
2245
- | gsarti/flores_101_jav | jav | byte_perplexity ↓ | 4.942 | 8.16 |
2246
- | gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.259 | 2.198 |
2247
- | gsarti/flores_101_kam | kam | byte_perplexity ↓ | 9.743 | 10.981 |
2248
- | gsarti/flores_101_kan | kan | byte_perplexity ↓ | 6.234 | 2.373 |
2249
- | gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.051 | 2.466 |
2250
- | gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.039 | 4.376 |
2251
- | gsarti/flores_101_kea | kea | byte_perplexity ↓ | 7.147 | 9.632 |
2252
- | gsarti/flores_101_khm | khm | byte_perplexity ↓ | 3.367 | 2.646 |
2253
- | gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.241 | 4.522 |
2254
- | gsarti/flores_101_kor | kor | byte_perplexity ↓ | 2.902 | 3.376 |
2255
- | gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.331 | 3.106 |
2256
- | gsarti/flores_101_lav | lav | byte_perplexity ↓ | 5.224 | 4.811 |
2257
- | gsarti/flores_101_lin | lin | byte_perplexity ↓ | 4.847 | 8.871 |
2258
- | gsarti/flores_101_lit | lit | byte_perplexity ↓ | 4.543 | 5.183 |
2259
- | gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 5.591 | 7.158 |
2260
- | gsarti/flores_101_lug | lug | byte_perplexity ↓ | 5.43 | 7.399 |
2261
- | gsarti/flores_101_luo | luo | byte_perplexity ↓ | 12.031 | 11.951 |
2262
- | gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.794 | 2.054 |
2263
- | gsarti/flores_101_mar | mar | byte_perplexity ↓ | 6.857 | 2.274 |
2264
- | gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.335 | 2.538 |
2265
- | gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 9.041 | 5.996 |
2266
- | gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.095 | 4.519 |
2267
- | gsarti/flores_101_mri | mri | byte_perplexity ↓ | 5.266 | 4.438 |
2268
- | gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.222 | 2.935 |
2269
- | gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.523 | 2.413 |
2270
- | gsarti/flores_101_nld | nld | byte_perplexity ↓ | 2.799 | 2.293 |
2271
- | gsarti/flores_101_nob | nob | byte_perplexity ↓ | 3.629 | 2.593 |
2272
- | gsarti/flores_101_npi | npi | byte_perplexity ↓ | 6.666 | 2.499 |
2273
- | gsarti/flores_101_nso | nso | byte_perplexity ↓ | 5.015 | 8.485 |
2274
- | gsarti/flores_101_nya | nya | byte_perplexity ↓ | 4.938 | 7.548 |
2275
- | gsarti/flores_101_oci | oci | byte_perplexity ↓ | 3.607 | 4.936 |
2276
- | gsarti/flores_101_orm | orm | byte_perplexity ↓ | 11.316 | 7.145 |
2277
- | gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.982 | 2.668 |
2278
- | gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.772 | 2.782 |
2279
- | gsarti/flores_101_pol | pol | byte_perplexity ↓ | 3.012 | 2.432 |
2280
- | gsarti/flores_101_por | por | byte_perplexity ↓ | 1.841 | 2.178 |
2281
- | gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.624 | 4.785 |
2282
- | gsarti/flores_101_ron | ron | byte_perplexity ↓ | 3.05 | 2.197 |
2283
- | gsarti/flores_101_rus | rus | byte_perplexity ↓ | 1.708 | 1.689 |
2284
- | gsarti/flores_101_slk | slk | byte_perplexity ↓ | 4.038 | 3.419 |
2285
- | gsarti/flores_101_slv | slv | byte_perplexity ↓ | 4.141 | 3.582 |
2286
- | gsarti/flores_101_sna | sna | byte_perplexity ↓ | 4.711 | 5.588 |
2287
- | gsarti/flores_101_snd | snd | byte_perplexity ↓ | 4.206 | 5.667 |
2288
- | gsarti/flores_101_som | som | byte_perplexity ↓ | 9.154 | 4.788 |
2289
- | gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.796 | 2.098 |
2290
- | gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.241 | 2.688 |
2291
- | gsarti/flores_101_swe | swe | byte_perplexity ↓ | 3.345 | 2.468 |
2292
- | gsarti/flores_101_swh | swh | byte_perplexity ↓ | 2.684 | 4.473 |
2293
- | gsarti/flores_101_tam | tam | byte_perplexity ↓ | 5.165 | 2.024 |
2294
- | gsarti/flores_101_tel | tel | byte_perplexity ↓ | 6.81 | 2.407 |
2295
- | gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.785 | 4.899 |
2296
- | gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 3.75 | 2.738 |
2297
- | gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.104 | 2.035 |
2298
- | gsarti/flores_101_tur | tur | byte_perplexity ↓ | 3.318 | 2.622 |
2299
- | gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.089 | 1.93 |
2300
- | gsarti/flores_101_umb | umb | byte_perplexity ↓ | 11.766 | 11.64 |
2301
- | gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.779 | 2.982 |
2302
- | gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 8.5 | 13.209 |
2303
- | gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.659 | 2.229 |
2304
- | gsarti/flores_101_wol | wol | byte_perplexity ↓ | 6.142 | 13.945 |
2305
- | gsarti/flores_101_xho | xho | byte_perplexity ↓ | 4.69 | 8.42 |
2306
- | gsarti/flores_101_yor | yor | byte_perplexity ↓ | 4.361 | 7.636 |
2307
- | gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.118 | 5.113 |
2308
- | gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.274 | 5.67 |
2309
- | gsarti/flores_101_zul | zul | byte_perplexity ↓ | 6.017 | 7.341 |
2310
- | headqa | esp | acc ↑ | 0.346 | 0.244 |
2311
- | hellaswag | eng | acc ↑ | 0.535 | 0.592 |
2312
- | lambada_mt_de | deu | acc ↑ | 0.329 | 0.358 |
2313
- | lambada_mt_en | eng | acc ↑ | 0.672 | 0.747 |
2314
- | lambada_mt_es | esp | acc ↑ | 0.476 | 0.397 |
2315
- | lambada_mt_it | ita | acc ↑ | 0.406 | 0.409 |
2316
- | logiqa | eng | acc ↑ | 0.235 | 0.244 |
2317
- | mathqa | eng | acc ↑ | 0.277 | 0.268 |
2318
- | mc_taco | eng | em ↑ | 0.131 | 0.124 |
2319
- | mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2320
- | mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2321
- | mrpc | eng | acc ↑ | 0.387 | 0.446 |
2322
- | multirc (Median of 11 prompts) | eng | acc ↑ | 0.571 | 0.599 |
2323
- | openbookqa | eng | acc ↑ | 0.312 | 0.322 |
2324
- | piqa | eng | acc ↑ | 0.781 | 0.791 |
2325
- | prost | eng | acc ↑ | 0.298 | 0.299 |
2326
- | pubmedqa | eng | acc ↑ | 0.741 | 0.709 |
2327
- | qnli | eng | acc ↑ | 0.517 | 0.554 |
2328
- | qqp (Median of 7 prompts) | eng | acc ↑ | 0.588 | 0.395 |
2329
- | race | eng | acc ↑ | 0.39 | 0.402 |
2330
- | rte (Median of 6 prompts) | eng | acc ↑ | 0.52 | 0.495 |
2331
- | sciq | eng | acc ↑ | 0.936 | 0.948 |
2332
- | sst (Median of 6 prompts) | eng | acc ↑ | 0.604 | 0.647 |
2333
- | triviaqa | eng | acc ↑ | 0.183 | 0.342 |
2334
- | tydiqa_primary (Median of 16 prompts) | eng | acc ↑ | 0.281 | 0.148 |
2335
- | webqs | eng | acc ↑ | 0.062 | 0.159 |
2336
- | wic (Median of 11 prompts) | eng | acc ↑ | 0.506 | 0.498 |
2337
- | winogrande | eng | acc ↑ | 0.71 | 0.736 |
2338
- | wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
2339
- | wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
2340
  | humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
2341
  | humaneval | python | pass@10 ↑ | 0.328 | 0.0 |
2342
  | humaneval | python | pass@100 ↑ | 0.572 | 0.003 |
 
172
  model-index:
173
  - name: bloom
174
  results:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
175
  - task:
176
  type: text-generation
177
  name: text generation
 
591
  ## Metrics
592
  *This section describes the different ways performance is calculated and why.*
593
 
 
594
  Includes:
595
 
596
  | Metric | Why chosen |
 
614
 
615
  **Zero-shot evaluations:**
616
 
617
+ <span style="color:red"><b>WARNING:</b> This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.</span>
618
 
619
  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
620
 
621
  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
622
+ |:--------|:-----------------|:------------------------|-------------:|------------:|jjj
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
623
  | humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
624
  | humaneval | python | pass@10 ↑ | 0.328 | 0.0 |
625
  | humaneval | python | pass@100 ↑ | 0.572 | 0.003 |