We lifted 13 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| google-research/multilingual-t5 | canonical | 5 of 5 |
| KoshiroSato/Flask_NLP_App | pwc_unofficial | 4 of 7 |
| MorenoLaQuatra/bart-it | pwc_unofficial | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| bert_predict | Ran | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/bert_model.py code served (permissive licence) · get_code("ab4db4a59f94ef40") |
| mt5_post_processing | Ran | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/mt5_model.py code served (permissive licence) · get_code("0d9ef0d0dfe26859") |
| normalize_mlqa | Ran | google-research/multilingual-t5/multilingual_t5/evaluation/metrics.py code served (permissive licence) · get_code("4ad4262f32bec052") |
| predict_with_post_processing | Ran | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/bert_model.py code served (permissive licence) · get_code("0d4436476da0cc30") |
| preprocess_logits_for_metrics | Ran | MorenoLaQuatra/bart-it/summarization/finetune_summarization.py code served (permissive licence) · get_code("2641f706223d1e5c") |
| read_txt | Ran | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/utils.py code served (permissive licence) · get_code("e445d0574fe31243") |
| span_f1 | Ran | google-research/multilingual-t5/multilingual_t5/evaluation/metrics.py code served (permissive licence) · get_code("3a70b94fccf1c165") |
| wikiann | Ran | google-research/multilingual-t5/multilingual_t5/preprocessors.py code served (permissive licence) · get_code("6a5b181d28b784b6") |
| xnli_map_hypothesis_premise | Ran | google-research/multilingual-t5/multilingual_t5/preprocessors.py code served (permissive licence) · get_code("f04d1c1706891df6") |
| xquad | Ran | google-research/multilingual-t5/multilingual_t5/preprocessors.py code served (permissive licence) · get_code("64c4f17ea92b71d7") |
| app_logger | Not yet run | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/logger.py code served (permissive licence) · get_code("d10b1ac136fbfec1") |
| model_io_logger | Not yet run | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/logger.py code served (permissive licence) · get_code("baf8e460eed595db") |
| read_yaml | Not yet run | KoshiroSato/Flask_NLP_App/flask/flaskapp/lib/utils.py code served (permissive licence) · get_code("c9737e28fdfdcfe9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent "accidental translation" in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.11934")
get_code_for_paper("2010.11934")
have("2010.11934")
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