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ru/index.html | 2 +- sitemap.xml | 440 +++---- zh-CN/index.html | 2 +- zh-TW/index.html | 2 +- 302 files changed, 2268 insertions(+), 4111 deletions(-) rename assets/{AboutUsPage.2e4a3fa0.js => AboutUsPage.4299ae34.js} (62%) rename assets/{ClosePopup.74fb6c4b.js => ClosePopup.925aed07.js} (90%) create mode 100644 assets/ContactUs.4c1c8a25.css rename assets/{ContactUs.30df480a.js => ContactUs.80569f89.js} (72%) delete mode 100644 assets/ContactUs.885bbadf.css delete mode 100644 assets/EmbeddingPage.1ef7b5ad.css rename assets/{EmbeddingPage.4b72c116.js => EmbeddingPage.47afcff7.js} (64%) create mode 100644 assets/EmbeddingPage.fdd155a4.css create mode 100644 assets/InternshipPage.67882417.css delete mode 100644 assets/InternshipPage.83b63c3a.css rename assets/{InternshipPage.8ff01b1c.js => InternshipPage.ac7fef38.js} (55%) delete mode 100644 assets/LabeledPanel.674be548.js create mode 100644 assets/LabeledPanel.98e8d86a.js rename assets/{LabeledPanel.3adc9c0b.css => 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assets/{index.ba5d1a68.js => index.241c62a7.js} (96%) rename assets/{position-engine.d24d93f9.js => position-engine.813e8c33.js} (98%) rename assets/{prism.b0a74f80.js => prism.e0eb0154.js} (85%) rename assets/{quasar-lang-pack.76f50e19.js => quasar-lang-pack.bcc6ccd7.js} (93%) rename assets/{register.61161271.js => register.703fda03.js} (99%) rename assets/{selection.f284f792.js => selection.678cf3d0.js} (80%) rename assets/{use-fullscreen.b4e56e4b.js => use-fullscreen.7937ac0c.js} (88%) rename assets/{useMetaTags.e5d2e45b.js => useMetaTags.0c88112a.js} (91%) diff --git a/404.html b/404.html index cdbd24476e1..2124d46196c 100644 --- a/404.html +++ b/404.html @@ -9,6 +9,6 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); - gtag('config', 'G-9T52NXDS9T'); + gtag('config', 'G-9T52NXDS9T');
\ No newline at end of file diff --git a/about-us/index.html b/about-us/index.html index f1f753e3d30..ecf5e1f9e45 100644 --- a/about-us/index.html +++ b/about-us/index.html @@ -1,6 +1,6 @@ -About Jina AI +About Jina AI -download-circle

This comprehensive guide will provide you with in-depth insights, case studies, and a clear roadmap on how SceneXplain can revolutionize your business's visual comprehension needs.

Conclusion: SceneXplain - The Future of Visual Comprehension

From digital marketing to e-commerce, news reporting to accessibility, SceneXplain is redefining boundaries. Its real-world impact is evident, and its potential is limitless. As industries evolve, SceneXplain stands ready to meet the challenges, driving innovation and inclusivity.

Ready to explore SceneXplain? Dive in and discover the future of visual comprehension. For more insights, check out our other articles and stay updated on the latest in AI image captioning.

SceneXplain - Leading AI Solution for Image Captions and Video Summaries
Experience cutting-edge computer vision with our premier image captioning and video summarization algorithms. Tailored for content creators, media professionals, SEO experts, and e-commerce enterprises. Featuring multilingual support and seamless API integration. Elevate your digital presence today.
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Case Study: Revolutionizing E-Commerce User Experience And Streamlining Search With SceneXplain
See how SceneXplain enhanced search quality, and enriched user experience for a top European e-commerce platform.
Miruna Nedelcu
October 30, 2023 • 3 minutes read
Unveiling the Magic: Become a Part of PromptPerfect's Affiliate Family
Introducing PromptPerfect's Affiliate Program, an initiative for enthusiasts to bring attention to Jina AI's innovative technologies and show our appreciation to the community that makes PromptPerfect great!
Miruna Nedelcu
October 18, 2023 • 3 minutes read
Graph Embedding 101: Unraveling the Magic of Relational Data
Graphs → everywhere. Social. Knowledge. Molecular. Critical infrastructure. Complex hairy ball visuals. Hard for machines. + gtag('config', 'G-9T52NXDS9T');
\ No newline at end of file +Now graph embeddings vectorize nodes. Distill graphs into geometry. Embeddings work magic. AI devours graphs.
Engineering Group
August 29, 2023 • 10 minutes read
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\ No newline at end of file diff --git a/news/scenexplain-vs-minigpt4-a-comprehensive-benchmark-of-top-5-image-captioning-algorithms-for-understanding-complex-scenes/index.html b/news/scenexplain-vs-minigpt4-a-comprehensive-benchmark-of-top-5-image-captioning-algorithms-for-understanding-complex-scenes/index.html index c94339e0085..74be347a51c 100644 --- a/news/scenexplain-vs-minigpt4-a-comprehensive-benchmark-of-top-5-image-captioning-algorithms-for-understanding-complex-scenes/index.html +++ b/news/scenexplain-vs-minigpt4-a-comprehensive-benchmark-of-top-5-image-captioning-algorithms-for-understanding-complex-scenes/index.html @@ -1,6 +1,6 @@ -SceneXplain vs. MiniGPT4: A Comprehensive Benchmark of Top 5 Image Captioning Algorithms for Understanding Complex Scenes +SceneXplain vs. MiniGPT4: A Comprehensive Benchmark of Top 5 Image Captioning Algorithms for Understanding Complex Scenes -download-circle
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References at Your Fingertips

Accompanying our infographic, we provide an extensive list of references, corresponding to each milestone depicted. This curated collection allows you to delve deeper into each technology, understanding the intricacies and applications that have shaped the field of natural language processing.

TF-IDF 1972 K.S. Jones, A statistical interpretation of term specificity and its application in retrieval, J. Doc. 28 (1972) 11–21.
TF-IDF 1973 K.S. Jones, Index term weighting, Inf. Storage Retr. 9 (11) (1973) 619–633.
Bag of Words 1981 Z.S. Harris, Distributional structure, in: Papers on Syntax, Springer, 1981, pp. 3–22.
BoN-Grams 1994 W. Cavnar, W.B. Cavnar, J.M. Trenkle, N-gram-based text categorization, in: Proceedings of 3rd Annual Symposium on Document Analysis and Information Retrieval (SDAIR-94), 1994, pp. 161–175.
doc2vec 2014 Q. Le, T. Mikolov, Distributed representations of sentences and documents, in: Proceedings of the 31st International Conference on International Conference on Machine Learning (ICML) - Volume 32, ICML ’14, JMLR.org, 2014, pp. II–1188–II–1196.
DAN 2015 M. Iyyer, V. Manjunatha, J. Boyd-Graber, H. Daumé III, Deep unordered composition rivals syntactic methods for text classification, in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Beijing, China, 2015, pp. 1681–1691.
RCNN 2015 S. Lai, L. Xu, K. Liu, J. Zhao, Recurrent convolutional neural networks for text classification, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI ’15, AAAI Press, 2015, pp. 2267–2273.
RNNs 2015 D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, in: International Conference on Learning Representations (ICLR) 2015, 2014.
Skip-Thought 2015 R. Kiros, Y. Zhu, R.R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, S. Fidler, Skip-thought vectors, in: C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 28, Curran Associates, Inc., 2015, pp. 3294–3302.
DESM 2016 E. Nalisnick, B. Mitra, N. Craswell, R. Caruana, Improving document rank- ing with dual word embeddings, in: Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 83–84.
DV-ngram 2016 B. Li, T. Liu, X. Du, D. Zhang, Z. Zhao, Learning document embeddings by predicting n-grams for sentiment classification of long movie reviews, in: Workshop Contribution at International Conference on Learning Representations (ICLR) 2016, 2016.
FastSent 2016 F. Hill, K. Cho, A. Korhonen, Learning distributed representations of sentences from unlabelled data, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics (ACL), San Diego, California, 2016, pp. 1367–1377.
HAN 2016 Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, Hierarchical attention networks for document classification, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, San Diego, California, 2016, pp. 1480–1489.
NVDM 2016 Y. Miao, L. Yu, P. Blunsom, Neural variational inference for text processing, in: Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML) - Volume 48, ICML ’16, JMLR.org, 2016, pp. 1727–1736.
Siamese CBoW 2016 T. Kenter, A. Borisov, M. de Rijke, Siamese CBOW: Optimizing word embed- dings for sentence representations, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics (ACL), Berlin, Germany, 2016, pp. 941–951.
CNN-LSTM 2017 Z. Gan, Y. Pu, R. Henao, C. Li, X. He, L. Carin, Learning generic sentence representations using convolutional neural networks, in: Empirical Methods in Natural Language Processing, EMNLP, 2017, pp. 2390–2400.
CNNs 2017 Y. Zhang, D. Shen, G. Wang, Z. Gan, R. Henao, L. Carin, Deconvolutional paragraph representation learning, in: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 30, Curran Associates, Inc., 2017, pp. 5438–5445.
CNNs 2017 Z. Zhu, J. Hu, Context aware document embedding, 2017, arXiv:1707.01521.
Doc2VecC 2017 M. Chen, Efficient vector representation for documents through corruption, in: International Conference on Learning Representations, ICLR, 2017.
DiSan 2018 T. Shen, T. Zhou, G. Long, J. Jiang, S. Pan, C. Zhang, DiSAN: Directional self- attention network for RNN/CNN-free language understanding, in: AAAI, 2018, pp. 5446–5455.
ELMo 2018 M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettle- moyer, Deep contextualized word representations, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Associa- tion for Computational Linguistics (ACL), New Orleans, Louisiana, 2018, pp. 2227–2237.
GPT-2 2018 A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, OpenAI Blog (2018).
ReSan 2018 T. Shen, T. Zhou, G. Long, J. Jiang, S. Wang, C. Zhang, Reinforced self-attention network: A hybrid of hard and soft attention for sequence modeling, in: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI ’18, AAAI Press, 2018, pp. 4345–4352.
Sent2vec 2018 M. Pagliardini, P. Gupta, M. Jaggi, Unsupervised learning of sentence embed- dings using compositional n-gram features, in: Proceedings of North American Chapter of the Association for Computational Linguistics NAACL-HLT, 2018, pp. 528–540.
BART 2019 Lewis, Mike, et al. "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension." arXiv preprint arXiv:1910.13461 (2019).
BERT 2019 J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidi- rectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics (ACL), Minneapolis, Minnesota, 2019, pp. 4171–4186.
DistilBERT 2019 V. Sanh, L. Debut, J. Chaumond, T. Wolf, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, in: 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019, 2019.
DocBERT 2019 A. Adhikari, A. Ram, R. Tang, J. Lin, DocBERT: BERT for document classification, 2019, ArXiv abs/1904.08398.
LASER 2019 M. Artetxe, H. Schwenk, Massively multilingual sentence embeddings for zero- shot cross-lingual transfer and beyond, Trans. Assoc. Comput. Linguist. 7 (2019) 597–610.
MASS 2019 K. Song, X. Tan, T. Qin, J. Lu, T. Liu, MASS: Masked sequence to sequence pre-training for language generation, in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, in: Proceedings of Machine Learning Research, vol. 97, PMLR, 2019, pp. 5926–5936.
SBERT 2019 N. Reimers, I. Gurevych, Sentence-BERT: Sentence embeddings using siamese BERT-networks, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 3982–3992.
Transformer-XL 2019 Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, R. Salakhutdinov, Transformer- XL: Attentive language models beyond a fixed-length context, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Association for Computational Linguistics, Florence, Italy, 2019, pp. 2978–2988.
VLAWE 2019 R.T. Ionescu, A. Butnaru, Vector of locally-aggregated word embeddings (VLAWE): A novel document-level representation, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics (ACL), Minneapolis, Minnesota, 2019, pp. 363–369.
XLM 2019 A. Conneau, G. Lample, Cross-lingual language model pretraining, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., 2019, pp. 7059–7069.
XLNet 2019 Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R.R. Salakhutdinov, Q.V. Le, XLNet: Generalized autoregressive pretraining for language understanding, in: H. Wal- lach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., 2019, pp. 5753–5763.
ALBERT 2020 Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, R. Soricut, ALBERT: A lite BERT for self-supervised learning of language representations, in: International Conference on Learning Representations, ICLR, OpenReview.net, 2020.
ELECTRA 2020 Clark, Kevin, et al. "Electra: Pre-training text encoders as discriminators rather than generators." arXiv preprint arXiv:2003.10555 (2020).
P-SIF 2020 V. Gupta, A. Saw, P. Nokhiz, P. Netrapalli, P. Rai, P. Talukdar, P-SIF: Document embeddings using partition averaging, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 7863–7870.
P-SIF 2020 V. Gupta, A. Kumar, P. Nokhiz, H. Gupta, P. Talukdar, Improving docu- ment classification with multi-sense embeddings, in: European Conference on Artificial Intelligence (ECAI) 2020, IOS Press, 2020, pp. 2030–2037.
RoBERTa 2020 Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L.Zettlemoyer, V. Stoyanov, RoBERTa: A robustly optimized BERT pretrainingapproach, in: Under Review as a Conference Paper at International Conference on Learning Representations (ICLR) 2020, 2020.
SpanBERT 2020 M. Joshi, D. Chen, Y. Liu, D. Weld, L. Zettlemoyer, O. Levy, SpanBERT: Improving pre-training by representing and predicting spans, Trans. Assoc. Comput. Linguist. 8 (2020).
SimCSE 2021 Tianyu Gao, Xingcheng Yao, and Danqi Chen. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910, Online and Punta Cana, Dominican Republic, 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.emnlp-main.552.
AugCSE 2022 Tang, Zilu, Muhammed Yusuf Kocyigit, and Derry Wijaya. "Augcse: Contrastive sentence embedding with diverse augmentations." arXiv preprint arXiv:2210.13749 (2022).
DiffCSE 2022 Oh, Dongsuk, et al. "Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling." arXiv preprint arXiv:2209.05972 (2022).
SGPT 2022 Muennighoff, Niklas. "Sgpt: Gpt sentence embeddings for semantic search." arXiv preprint arXiv:2202.08904 (2022).
bge 2023 C-Pack: Packaged Resources To Advance General Chinese Embedding
embeddings-v2 2023 Günther, Michael, et al. "Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents." arXiv preprint arXiv:2310.19923 (2023).
Categories:
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Tech blog
Learn more
Using Jina Embeddings v2 with Haystack Pipelines
Access Jina AI's state-of-the-art open-source embedding models in your Haystack application pipeline.
Saahil Ognawala, Scott Martens
January 19, 2024 • 1 minutes read
Words + JSON + Images = SceneXplain's new JSON Schema Builder
Effortlessly create JSON Schemas with SceneXplain: Describe your needs in natural language, and get the perfect schema for extracting JSON from your images!
Alex C-G
January 04, 2024 • 2 minutes read
Full-stack RAG with Jina Embeddings v2 and LlamaIndex
You can build your own RAG chatbot in a matter of minutes with Jina Embeddings, LlamaIndex and Mixtral Instruct. We'll show you how to get up and running right now.
Scott Martens
December 22, 2023 • 12 minutes read
Offices
Berlin, Germany (HQ)
Ohlauer Str. 43 (1st floor), zone A, 10999 Berlin, Germany
Geschäftsanschrift: Leipziger str. 96, 10117 Berlin, Germany
Beijing, China
Level 5, Building 6, No.48 Haidian West St. Beijing Haidian, China
Shenzhen, China
402, Floor 4, Fu'an Technology Building, Shenzhen Nanshan, China
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References at Your Fingertips

Accompanying our infographic, we provide an extensive list of references, corresponding to each milestone depicted. This curated collection allows you to delve deeper into each technology, understanding the intricacies and applications that have shaped the field of natural language processing.

TF-IDF 1972 K.S. Jones, A statistical interpretation of term specificity and its application in retrieval, J. Doc. 28 (1972) 11–21.
TF-IDF 1973 K.S. Jones, Index term weighting, Inf. Storage Retr. 9 (11) (1973) 619–633.
Bag of Words 1981 Z.S. Harris, Distributional structure, in: Papers on Syntax, Springer, 1981, pp. 3–22.
BoN-Grams 1994 W. Cavnar, W.B. Cavnar, J.M. Trenkle, N-gram-based text categorization, in: Proceedings of 3rd Annual Symposium on Document Analysis and Information Retrieval (SDAIR-94), 1994, pp. 161–175.
doc2vec 2014 Q. Le, T. Mikolov, Distributed representations of sentences and documents, in: Proceedings of the 31st International Conference on International Conference on Machine Learning (ICML) - Volume 32, ICML ’14, JMLR.org, 2014, pp. II–1188–II–1196.
DAN 2015 M. Iyyer, V. Manjunatha, J. Boyd-Graber, H. Daumé III, Deep unordered composition rivals syntactic methods for text classification, in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Beijing, China, 2015, pp. 1681–1691.
RCNN 2015 S. Lai, L. Xu, K. Liu, J. Zhao, Recurrent convolutional neural networks for text classification, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI ’15, AAAI Press, 2015, pp. 2267–2273.
RNNs 2015 D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, in: International Conference on Learning Representations (ICLR) 2015, 2014.
Skip-Thought 2015 R. Kiros, Y. Zhu, R.R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, S. Fidler, Skip-thought vectors, in: C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 28, Curran Associates, Inc., 2015, pp. 3294–3302.
DESM 2016 E. Nalisnick, B. Mitra, N. Craswell, R. Caruana, Improving document rank- ing with dual word embeddings, in: Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 83–84.
DV-ngram 2016 B. Li, T. Liu, X. Du, D. Zhang, Z. Zhao, Learning document embeddings by predicting n-grams for sentiment classification of long movie reviews, in: Workshop Contribution at International Conference on Learning Representations (ICLR) 2016, 2016.
FastSent 2016 F. Hill, K. Cho, A. Korhonen, Learning distributed representations of sentences from unlabelled data, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics (ACL), San Diego, California, 2016, pp. 1367–1377.
HAN 2016 Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, Hierarchical attention networks for document classification, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, San Diego, California, 2016, pp. 1480–1489.
NVDM 2016 Y. Miao, L. Yu, P. Blunsom, Neural variational inference for text processing, in: Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML) - Volume 48, ICML ’16, JMLR.org, 2016, pp. 1727–1736.
Siamese CBoW 2016 T. Kenter, A. Borisov, M. de Rijke, Siamese CBOW: Optimizing word embed- dings for sentence representations, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics (ACL), Berlin, Germany, 2016, pp. 941–951.
CNN-LSTM 2017 Z. Gan, Y. Pu, R. Henao, C. Li, X. He, L. Carin, Learning generic sentence representations using convolutional neural networks, in: Empirical Methods in Natural Language Processing, EMNLP, 2017, pp. 2390–2400.
CNNs 2017 Y. Zhang, D. Shen, G. Wang, Z. Gan, R. Henao, L. Carin, Deconvolutional paragraph representation learning, in: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 30, Curran Associates, Inc., 2017, pp. 5438–5445.
CNNs 2017 Z. Zhu, J. Hu, Context aware document embedding, 2017, arXiv:1707.01521.
Doc2VecC 2017 M. Chen, Efficient vector representation for documents through corruption, in: International Conference on Learning Representations, ICLR, 2017.
DiSan 2018 T. Shen, T. Zhou, G. Long, J. Jiang, S. Pan, C. Zhang, DiSAN: Directional self- attention network for RNN/CNN-free language understanding, in: AAAI, 2018, pp. 5446–5455.
ELMo 2018 M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettle- moyer, Deep contextualized word representations, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Associa- tion for Computational Linguistics (ACL), New Orleans, Louisiana, 2018, pp. 2227–2237.
GPT-2 2018 A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, OpenAI Blog (2018).
ReSan 2018 T. Shen, T. Zhou, G. Long, J. Jiang, S. Wang, C. Zhang, Reinforced self-attention network: A hybrid of hard and soft attention for sequence modeling, in: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI ’18, AAAI Press, 2018, pp. 4345–4352.
Sent2vec 2018 M. Pagliardini, P. Gupta, M. Jaggi, Unsupervised learning of sentence embed- dings using compositional n-gram features, in: Proceedings of North American Chapter of the Association for Computational Linguistics NAACL-HLT, 2018, pp. 528–540.
BART 2019 Lewis, Mike, et al. "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension." arXiv preprint arXiv:1910.13461 (2019).
BERT 2019 J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidi- rectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics (ACL), Minneapolis, Minnesota, 2019, pp. 4171–4186.
DistilBERT 2019 V. Sanh, L. Debut, J. Chaumond, T. Wolf, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, in: 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019, 2019.
DocBERT 2019 A. Adhikari, A. Ram, R. Tang, J. Lin, DocBERT: BERT for document classification, 2019, ArXiv abs/1904.08398.
LASER 2019 M. Artetxe, H. Schwenk, Massively multilingual sentence embeddings for zero- shot cross-lingual transfer and beyond, Trans. Assoc. Comput. Linguist. 7 (2019) 597–610.
MASS 2019 K. Song, X. Tan, T. Qin, J. Lu, T. Liu, MASS: Masked sequence to sequence pre-training for language generation, in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, in: Proceedings of Machine Learning Research, vol. 97, PMLR, 2019, pp. 5926–5936.
SBERT 2019 N. Reimers, I. Gurevych, Sentence-BERT: Sentence embeddings using siamese BERT-networks, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 3982–3992.
Transformer-XL 2019 Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, R. Salakhutdinov, Transformer- XL: Attentive language models beyond a fixed-length context, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Association for Computational Linguistics, Florence, Italy, 2019, pp. 2978–2988.
VLAWE 2019 R.T. Ionescu, A. Butnaru, Vector of locally-aggregated word embeddings (VLAWE): A novel document-level representation, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics (ACL), Minneapolis, Minnesota, 2019, pp. 363–369.
XLM 2019 A. Conneau, G. Lample, Cross-lingual language model pretraining, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., 2019, pp. 7059–7069.
XLNet 2019 Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R.R. Salakhutdinov, Q.V. Le, XLNet: Generalized autoregressive pretraining for language understanding, in: H. Wal- lach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., 2019, pp. 5753–5763.
ALBERT 2020 Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, R. Soricut, ALBERT: A lite BERT for self-supervised learning of language representations, in: International Conference on Learning Representations, ICLR, OpenReview.net, 2020.
ELECTRA 2020 Clark, Kevin, et al. "Electra: Pre-training text encoders as discriminators rather than generators." arXiv preprint arXiv:2003.10555 (2020).
P-SIF 2020 V. Gupta, A. Saw, P. Nokhiz, P. Netrapalli, P. Rai, P. Talukdar, P-SIF: Document embeddings using partition averaging, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 7863–7870.
P-SIF 2020 V. Gupta, A. Kumar, P. Nokhiz, H. Gupta, P. Talukdar, Improving docu- ment classification with multi-sense embeddings, in: European Conference on Artificial Intelligence (ECAI) 2020, IOS Press, 2020, pp. 2030–2037.
RoBERTa 2020 Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L.Zettlemoyer, V. Stoyanov, RoBERTa: A robustly optimized BERT pretrainingapproach, in: Under Review as a Conference Paper at International Conference on Learning Representations (ICLR) 2020, 2020.
SpanBERT 2020 M. Joshi, D. Chen, Y. Liu, D. Weld, L. Zettlemoyer, O. Levy, SpanBERT: Improving pre-training by representing and predicting spans, Trans. Assoc. Comput. Linguist. 8 (2020).
SimCSE 2021 Tianyu Gao, Xingcheng Yao, and Danqi Chen. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910, Online and Punta Cana, Dominican Republic, 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.emnlp-main.552.
AugCSE 2022 Tang, Zilu, Muhammed Yusuf Kocyigit, and Derry Wijaya. "Augcse: Contrastive sentence embedding with diverse augmentations." arXiv preprint arXiv:2210.13749 (2022).
DiffCSE 2022 Oh, Dongsuk, et al. "Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling." arXiv preprint arXiv:2209.05972 (2022).
SGPT 2022 Muennighoff, Niklas. "Sgpt: Gpt sentence embeddings for semantic search." arXiv preprint arXiv:2202.08904 (2022).
bge 2023 C-Pack: Packaged Resources To Advance General Chinese Embedding
embeddings-v2 2023 Günther, Michael, et al. "Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents." arXiv preprint arXiv:2310.19923 (2023).
Categories:
Featured
Tech blog
Learn more
Using Jina Embeddings v2 with Haystack Pipelines
Access Jina AI's state-of-the-art open-source embedding models in your Haystack application pipeline.
Saahil Ognawala, Scott Martens
January 19, 2024 • 1 minutes read
Words + JSON + Images = SceneXplain's new JSON Schema Builder
Effortlessly create JSON Schemas with SceneXplain: Describe your needs in natural language, and get the perfect schema for extracting JSON from your images!
Alex C-G
January 04, 2024 • 2 minutes read
Full-stack RAG with Jina Embeddings v2 and LlamaIndex
You can build your own RAG chatbot in a matter of minutes with Jina Embeddings, LlamaIndex and Mixtral Instruct. We'll show you how to get up and running right now.
Scott Martens
December 22, 2023 • 12 minutes read
Offices
Berlin, Germany (HQ)
Ohlauer Str. 43 (1st floor), zone A, 10999 Berlin, Germany
Geschäftsanschrift: Leipziger str. 96, 10117 Berlin, Germany
Beijing, China
Level 5, Building 6, No.48 Haidian West St. Beijing Haidian, China
Shenzhen, China
402, Floor 4, Fu'an Technology Building, Shenzhen Nanshan, China
© Jina AI GmbH 2020-2024. All rights reserved.
\ No newline at end of file diff --git a/news/the-boundless-horizon-of-ai-its-not-just-about-the-size/index.html b/news/the-boundless-horizon-of-ai-its-not-just-about-the-size/index.html index c142c3b732e..2a2441d27bc 100644 --- a/news/the-boundless-horizon-of-ai-its-not-just-about-the-size/index.html +++ b/news/the-boundless-horizon-of-ai-its-not-just-about-the-size/index.html @@ -1,6 +1,6 @@ -Beyond Sheer Scale: Navigating AI Alignment Odyssey +Beyond Sheer Scale: Navigating AI Alignment Odyssey -