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bigscience
/
bloomz-p3
like
14
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BigScience Workshop
796
Text Generation
Transformers
PyTorch
TensorBoard
Muennighoff/P3
46 languages
bloom
Eval Results
text-generation-inference
arxiv:
2211.01786
License:
bigscience-bloom-rail-1.0
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aa2424b
bloomz-p3
/
evaluation_val
/
wmt14_hi_en
4 contributors
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1 commit
Muennighoff
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about 2 years ago
agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:24.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:08.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:35.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:57.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:34:35.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:37:07.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:32:56.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:51.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:33.json
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agg.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:47.json
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:24.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:08.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:35.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:57.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:34:35.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:37:07.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:32:56.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:51.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:33.jsonl
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about 2 years ago
examples.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:47.jsonl
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:24.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:08.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-source+target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:35.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=a_good_translation-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:57.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:34:35.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=gpt-3-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:37:07.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:32:56.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=version-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:35:51.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-en-hi-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:33:33.json
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about 2 years ago
slim.limited=3000.model=p31lossseq_global_step498.task=wmt14_hi_en.templates=xglm-hi-en-target.fewshot=0.batchsize=4.seed=1234.timestamp=2022-09-12T20:36:47.json
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about 2 years ago