FARM
BERT-NER
| FARM | BERT-NER | |
|---|---|---|
| 3 | 1 | |
| 1,723 | 1,182 | |
| 0.3% | - | |
| 0.0 | 0.0 | |
| 4 months ago | almost 3 years ago | |
| Python | Python | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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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.
BERT-NER
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?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
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.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).
sparktorch - Train and run Pytorch models on Apache Spark.