Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| T5 (Text-to-Text Transfer Transformer)× | Attention Mechanism× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2020 | 2015 |
| Mwanzilishi≠ | Raffel, C.; Shazeer, N.; Roberts, A.; et al. (Google Brain) | Bahdanau, D.; Luong, M.T. |
| Aina≠ | Pre-trained encoder-decoder Transformer (sequence-to-sequence) | Neural attention layer (encoder-decoder) |
| Chanzo asilia≠ | Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ |
| Majina mbadala≠ | T5, Text-to-Text Transfer Transformer, T5-Small, T5-Base | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention |
| Zinazohusiana≠ | 2 | 5 |
| Muhtasari≠ | T5 is a unified sequence-to-sequence deep learning framework introduced by Raffel et al. at Google Brain in 2020, published in the Journal of Machine Learning Research (Vol. 21, No. 140). It reframes every NLP task — classification, translation, summarisation, question answering, and more — as a text-to-text problem: both input and output are always character strings, enabling a single encoder-decoder Transformer to be pre-trained once and fine-tuned across tasks with a consistent interface. T5 introduced span-corruption pre-training and the C4 corpus, and its largest variant (11B parameters) achieved state-of-the-art results across a wide range of NLP benchmarks at the time of publication. | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. |
| ScholarGateSeti ya data ↗ |
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