happy-transformer
FARM
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| happy-transformer | FARM | |
|---|---|---|
| 1 | 3 | |
| 497 | 1,723 | |
| - | 0.5% | |
| 9.0 | 0.0 | |
| about 1 month ago | 4 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
happy-transformer
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GPT-Neo-125M-AID (Mia) oversight + retrained
This appears to be an actual issue with Happy Transformer judging by a GitHub issue I've found of the same problem.
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.
What are some alternatives?
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
small-text - Active Learning for Text Classification in Python
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
gector - Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).