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Text Generation
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text-generation-inference
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Let's merge this for now, but maybe I could turn it into a table like below once all are finished. Thoughts?

`"| Task | Language | Metric | BLOOM-350M | BLOOM-750M | BLOOM-1B3 | BLOOM-2B5 | BLOOM-6B3 | BLOOM-176B | OPT-176B |"`

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  1. README.md +1690 -4
README.md CHANGED
@@ -50,6 +50,1546 @@ language:
50
  - zht
51
  - zu
52
  pipeline_tag: text-generation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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>&nbsp;</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
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+ - 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
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+ verified: false
78
+ - task:
79
+ type: text-generation
80
+ name: text generation
81
+ dataset:
82
+ name: axb
83
+ type: axb
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+ metrics:
85
+ - name: acc
86
+ type: acc
87
+ value: 0.4433876811594203
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+ 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
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+ type: acc
109
+ value: 0.6165137614678899
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+ verified: false
111
+ - task:
112
+ type: text-generation
113
+ name: text generation
114
+ dataset:
115
+ name: cb
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+ type: cb
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+ metrics:
118
+ - name: acc
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+ type: acc
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+ value: 0.30357142857142855
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+ 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
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+ 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
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+ verified: false
166
+ - task:
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+ type: text-generation
168
+ name: text generation
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+ dataset:
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+ name: diabla
171
+ type: diabla
172
+ metrics:
173
+ - name: acc
174
+ type: acc
175
+ value: 0.28888308977035493
176
+ verified: false
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+ - task:
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+ 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
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+ 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
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+ verified: false
210
+ - task:
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+ 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
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+ 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
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
236
+ name: gsarti/flores_101_azj
237
+ type: gsarti/flores_101_azj
238
+ metrics:
239
+ - name: byte_perplexity
240
+ type: byte_perplexity
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+ value: 6.942805054270002
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ 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
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+ verified: false
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+ - task:
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+ type: text-generation
256
+ name: text generation
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+ dataset:
258
+ name: gsarti/flores_101_ben
259
+ type: gsarti/flores_101_ben
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 5.121491534300969
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
269
+ name: gsarti/flores_101_bos
270
+ type: gsarti/flores_101_bos
271
+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 5.653353469118798
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ 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
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+ verified: false
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+ - task:
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+ 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
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+ 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:
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+ - name: byte_perplexity
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+ type: byte_perplexity
307
+ value: 6.291000321323428
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+ verified: false
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+ - 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
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+ verified: false
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+ - task:
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+ type: text-generation
322
+ name: text generation
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+ 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
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+ verified: false
331
+ - task:
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+ type: text-generation
333
+ name: text generation
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+ 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:
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+ type: text-generation
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+ 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
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+ verified: false
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+ - task:
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+ type: text-generation
355
+ name: text generation
356
+ dataset:
357
+ name: gsarti/flores_101_deu
358
+ type: gsarti/flores_101_deu
359
+ metrics:
360
+ - name: byte_perplexity
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+ type: byte_perplexity
362
+ value: 3.1180422286591347
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
368
+ name: gsarti/flores_101_ell
369
+ type: gsarti/flores_101_ell
370
+ metrics:
371
+ - name: byte_perplexity
372
+ type: byte_perplexity
373
+ value: 2.467943456164706
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+ verified: false
375
+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
379
+ name: gsarti/flores_101_eng
380
+ type: gsarti/flores_101_eng
381
+ metrics:
382
+ - name: byte_perplexity
383
+ type: byte_perplexity
384
+ value: 2.018740628193298
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+ verified: false
386
+ - task:
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+ type: text-generation
388
+ name: text generation
389
+ dataset:
390
+ name: gsarti/flores_101_est
391
+ type: gsarti/flores_101_est
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+ metrics:
393
+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 9.11654425176368
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+ verified: false
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+ - task:
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+ type: text-generation
399
+ name: text generation
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+ dataset:
401
+ name: gsarti/flores_101_fas
402
+ type: gsarti/flores_101_fas
403
+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 3.058009097116482
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
412
+ name: gsarti/flores_101_fin
413
+ type: gsarti/flores_101_fin
414
+ metrics:
415
+ - name: byte_perplexity
416
+ type: byte_perplexity
417
+ value: 6.847047959628553
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+ verified: false
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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: gsarti/flores_101_fra
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+ type: gsarti/flores_101_fra
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 1.9975177011840075
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+ verified: false
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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: gsarti/flores_101_ful
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+ type: gsarti/flores_101_ful
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
439
+ value: 11.465912731488828
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
444
+ dataset:
445
+ name: gsarti/flores_101_gle
446
+ type: gsarti/flores_101_gle
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 8.681491663539422
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+ verified: false
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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: gsarti/flores_101_glg
457
+ type: gsarti/flores_101_glg
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+ metrics:
459
+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 3.029991089015508
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
467
+ name: gsarti/flores_101_guj
468
+ type: gsarti/flores_101_guj
469
+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 4.955224230286231
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
478
+ name: gsarti/flores_101_hau
479
+ type: gsarti/flores_101_hau
480
+ metrics:
481
+ - name: byte_perplexity
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+ type: byte_perplexity
483
+ value: 10.758347356372159
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+ verified: false
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+ - task:
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+ type: text-generation
487
+ name: text generation
488
+ dataset:
489
+ name: gsarti/flores_101_heb
490
+ type: gsarti/flores_101_heb
491
+ metrics:
492
+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 3.6004478129801667
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+ verified: false
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+ - task:
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+ type: text-generation
498
+ name: text generation
499
+ dataset:
500
+ name: gsarti/flores_101_hin
501
+ type: gsarti/flores_101_hin
502
+ metrics:
503
+ - name: byte_perplexity
504
+ type: byte_perplexity
505
+ value: 4.712530650588064
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+ verified: false
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+ - task:
508
+ type: text-generation
509
+ name: text generation
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+ dataset:
511
+ name: gsarti/flores_101_hrv
512
+ type: gsarti/flores_101_hrv
513
+ metrics:
514
+ - name: byte_perplexity
515
+ type: byte_perplexity
516
+ value: 5.822418943372185
517
+ verified: false
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+ - task:
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+ type: text-generation
520
+ name: text generation
521
+ dataset:
522
+ name: gsarti/flores_101_hun
523
+ type: gsarti/flores_101_hun
524
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+ name: text generation
1159
+ dataset:
1160
+ name: gsarti/flores_101_tur
1161
+ type: gsarti/flores_101_tur
1162
+ metrics:
1163
+ - name: byte_perplexity
1164
+ type: byte_perplexity
1165
+ value: 4.885014749844601
1166
+ verified: false
1167
+ - task:
1168
+ type: text-generation
1169
+ name: text generation
1170
+ dataset:
1171
+ name: gsarti/flores_101_ukr
1172
+ type: gsarti/flores_101_ukr
1173
+ metrics:
1174
+ - name: byte_perplexity
1175
+ type: byte_perplexity
1176
+ value: 2.7240934990288483
1177
+ verified: false
1178
+ - task:
1179
+ type: text-generation
1180
+ name: text generation
1181
+ dataset:
1182
+ name: gsarti/flores_101_umb
1183
+ type: gsarti/flores_101_umb
1184
+ metrics:
1185
+ - name: byte_perplexity
1186
+ type: byte_perplexity
1187
+ value: 12.766915508610673
1188
+ verified: false
1189
+ - task:
1190
+ type: text-generation
1191
+ name: text generation
1192
+ dataset:
1193
+ name: gsarti/flores_101_urd
1194
+ type: gsarti/flores_101_urd
1195
+ metrics:
1196
+ - name: byte_perplexity
1197
+ type: byte_perplexity
1198
+ value: 1.9797467071381232
1199
+ verified: false
1200
+ - task:
1201
+ type: text-generation
1202
+ name: text generation
1203
+ dataset:
1204
+ name: gsarti/flores_101_uzb
1205
+ type: gsarti/flores_101_uzb
1206
+ metrics:
1207
+ - name: byte_perplexity
1208
+ type: byte_perplexity
1209
+ value: 12.002337637722146
1210
+ verified: false
1211
+ - task:
1212
+ type: text-generation
1213
+ name: text generation
1214
+ dataset:
1215
+ name: gsarti/flores_101_vie
1216
+ type: gsarti/flores_101_vie
1217
+ metrics:
1218
+ - name: byte_perplexity
1219
+ type: byte_perplexity
1220
+ value: 1.76578415476397
1221
+ verified: false
1222
+ - 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>&nbsp;</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