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Copyright 2023, llmware
This software contains links to the llmware public model repository. Models in this repository are licensed under the Apache License 2.0. (https://www.apache.org/licenses/LICENSE-2.0)
This software contains code copied from, derived from or inspired by Nils Reimers and the UKP Labe Sentence Transformers Model. (https://github.com/UKPLab/sentence-transformers)
Copyright 2019 Nils Reimers
This software contains code copied from, derived from or inspired from the PyTorch BERT model. (https://github.com/huggingface/transformers)
Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
This software contains code copied from, derived from or inspired from the Huggingface transformers generation code. (https://github.com/huggingface/transformers/src/transformers/generation)
Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
=================================================================================================
Open-Source dependencies for the llmware package:
3-clause BSD License (https://opensource.org/license/bsd-3-clause/)
* Software: libzip (https://libzip.org/)
* Software: lxml (https://github.com/lxml/lxml)
* Software: numpy (https://github.com/numpy/numpy)
* Software: pandas (https://github.com/pandas-dev/pandas)
* Software: scikit-learn (https://github.com/scikit-learn/scikit-learn)
* Software: scipy (https://github.com/scipy/scipy)
* Software: torch (https://github.com/pytorch/pytorch)
* Software: Werkzeug (https://github.com/pallets/werkzeug/)
Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0)
* Software: boto3 (https://github.com/boto/boto3)
* Software: google-cloud-aiplatform (https://github.com/googleapis/python-aiplatform)
* Software: mongo-c-driver (https://github.com/mongodb/mongo-c-driver)
* Software: pymilvus (https://github.com/milvus-io/pymilvus)
* Software: pymongo (http://github.com/mongodb/mongo-python-driver)
* Software: pytesseract (https://github.com/madmaze/pytesseract)
* Software: tesseract (https://github.com/tesseract-ocr/tesseract)
* Software: tokenizers (https://github.com/huggingface/tokenizers)
* Software: yfinance (https://github.com/ranaroussi/yfinance)
GNU GENERAL PUBLIC LICENSE 3.0 (https://www.gnu.org/licenses/gpl-3.0.html#license-text)
* Software: poppler (https://poppler.freedesktop.org/)
Historical Permission Notice and Disclaimer (HPND) (https://spdx.org/licenses/HPND)
* Software: pillow (https://github.com/python-pillow/Pillow)
libtiff License (https://spdx.org/licenses/libtiff.html)
* Softward: libtiff (http://www.libtiff.org/)
MIT License (https://opensource.org/license/mit/)
* Software: ai21 (https://pypi.org/project/ai21/)
* Software: anthropic (https://github.com/anthropics/anthropic-sdk-python)
* Software: beautifulsoup4 (https://pypi.org/project/beautifulsoup4/)
* Software: cohere (https://github.com/cohere-ai/cohere-python)
* Software: faise-cpu (https://github.com/kyamagu/faiss-wheels)
* Software: openai (https://github.com/openai/openai-python)
* Software: pdf2image (https://github.com/Belval/pdf2image)
* Software: word2number (https://github.com/akshaynagpal/w2n)
* Software: Wikipedia-API (https://github.com/martin-majlis/Wikipedia-API)
PNG Reference Library version 2 (https://spdx.org/licenses/libpng-2.0.html)
* Software: libpng (http://www.libpng.org/pub/png/libpng.html)
zlib License (https://github.com/madler/zlib/blob/develop/LICENSE)
* Software: zlib (https://www.zlib.net/)
=================================================================================================
Citations for Open Source Software and Research used in the development of llmware:
NumPy
Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Tokenizers
Moi, A., & Patry, N. (2023). HuggingFace's Tokenizers (Version 0.13.4) [Computer software]. https://github.com/huggingface/tokenizers
Torch
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library [Conference paper]. Advances in Neural Information Processing Systems 32, 8024–8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
BERT
Turc, Iulia; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina. Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, arXiv preprint arXiv:1908.08962v2 (2019).
Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, arXiv preprint arXiv:1810.04805 (2018).
GPT2
Radford, Alec; Wu, Jeff; Child, Rewon; Luan, David; Amodei, Dario; Sutskever, Ilya. Language Models are Unsupervised Multitask Learners. (2019)
Roberta
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach. CoRR, abs/1907.11692, 2019. http://arxiv.org/abs/1907.11692.
Sentence-BERT
Reimers, Nils and Gurevych, Iryna. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. November 2019. Association for Computational Linguistics. https://arxiv.org/abs/1908.10084.
Huggingface Transformers
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., & Rush, A. M. (2020). Transformers: State-of-the-Art Natural Language Processing [Conference paper]. 38–45. https://www.aclweb.org/anthology/2020.emnlp-demos.6