FARM
haystack
| FARM | haystack | |
|---|---|---|
| 3 | 55 | |
| 1,723 | 13,711 | |
| 0.3% | 2.5% | |
| 0.0 | 9.9 | |
| 4 months ago | about 15 hours ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FARM
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Can someone please explain to me the differences between train, dev and test datasets?
I'm also trying to solve this task in a python notebook (.ipynb) using the FARM framework https://farm.deepset.ai/ and BERT model of huggingface https://huggingface.co/bert-base-uncased
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Fine-Tuning Transformers for NLP
For anyone looking to fine-train transformers with less work, there is the FARM project (https://github.com/deepset-ai/FARM) which has some more or less ready-to-go configurations (classification, question answering, NER, and a couple of others). It's really almost "plug in a csv and run".
By the way, a pet peeve is sentiment detection. It's a useful method, but please be aware that it does not measure "sentiment" in a way that one would normally think, and that what it measure varies strongly across methods (https://www.tandfonline.com/doi/abs/10.1080/19312458.2020.18...).
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Has anyone deployed a BERT like model across multiple tasks (Multi-class, NER, outlier detection)? Seeking advice.
You can use https://github.com/deepset-ai/FARM or https://github.com/nyu-mll/jiant for multitask learning. The second is more general.
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
langchain - 🦜🔗 Build context-aware reasoning applications
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).
jina - ☁️ Build multimodal AI applications with cloud-native stack