विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| अनुक्रम-से-अनुक्रम मॉडल× | BERT फाइन-ट्यूनिंग× | बहु-शीर्षक स्व-ध्यान (Multi-Head Self-Attention)× | |
|---|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2014 | 2019 | 2017 |
| प्रवर्तक≠ | Sutskever, I.; Cho, K. | Devlin, J. et al. | Vaswani, A. et al. |
| प्रकार≠ | Encoder-decoder neural network (deep learning) | Transfer learning (fine-tuning a pre-trained transformer) | Attention mechanism (Transformer core) |
| मौलिक स्रोत≠ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| उपनाम | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention |
| संबंधित | 5 | 5 | 5 |
| सारांश≠ | The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation. | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. |
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