flair
spaCy
Our great sponsors
flair | spaCy | |
---|---|---|
9 | 106 | |
13,558 | 28,704 | |
1.0% | 1.3% | |
9.4 | 9.2 | |
6 days ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
flair
- Flair: A simple framework for state-of-the-art Natural Language Processing
-
Artificial Intelligence sentiment analysis of the Harry Potter movies. The greener the edge the happier the conversations, the bigger the edge the more they talk. Made by me.
The code of the module is available there for easy access: https://github.com/flairNLP/flair
-
The Spacy NER model for Spanish is terrible
Had the same experience with the german model in spacy (but tbh, the quailty of my textdata was bad). A bert based approach with flair really improved my results. I think there is a spanish pretrained model also available
-
How to create a dataset for training NER models when you only have entity data
We have a list of entities in text files separated with a new line. We intend to train the flair model to detect these entities in text, but NER models require the entity to be labeled in a paragraph with BOI format.
-
Preparing data for training NER models
Training most of the Named Entity Recognition (NER) models for example Flair usually needs to format data in BOI tagging) scheme as shown below where each sentence is separated by blank line
-
German POS Corpus for Commercial use
I had the same problem a couple years ago. I think Flair, form Zalando uses a different Corpus. However, it's not great and I am pretty sure they are infringing the license anyway...
-
Advice for how to approach classifying apartment posts on facebook?
For example, my first approach to the pet sentences would be to label all sentences within a respective text corpus containing according information for either yes or no. You would then convert this to a tertiary tag set, something like ["pet allowed", "pet not allowed", "irrelevant"]. You could then try out a model based on SentenceBert, other sentence-level embeddings/language models or 1D CNNs for this. flairNLP (https://github.com/flairNLP/flair) is a small, little framework which provides comfortable high-level access to different common language models which integrates perfectly with pyTorch.
-
SpaCy VS Transformers for NER
For NER, if you don't need the full toolkit of spacy, I'd highly recommend checking out Flair. It will likely run faster than transformer-based models (like en_core_web_trf) and it tends to be one of the best performing approaches to NER.
-
[D] NLP Q: How to extract this part from a messy short text?
You then train the whole thing on sequences where each position has a label that is begin/inside/outside and thus you can calculate cross-entropy loss. So all in all it is basically: https://github.com/flairNLP/flair, https://huggingface.co/transformers/model_doc/distilbert.html#tfdistilbertforsequenceclassification or any huggingface model "for sequence classificaiton" or but just char based instead of word based. The CRF layer (as included in flair) is optional but may be useful.
spaCy
-
Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
-
Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
-
Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
-
A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
-
Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
-
How to predict this sequence?
spaCy
-
What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
- spaCy: Industrial-Strength Natural Language Processing
What are some alternatives?
spacy-models - 💫 Models for the spaCy Natural Language Processing (NLP) library
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
NLTK - NLTK Source
gensim - Topic Modelling for Humans
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
polyglot - Multilingual text (NLP) processing toolkit
MAX-Toxic-Comment-Classifier - Detect 6 types of toxicity in user comments.
textacy - NLP, before and after spaCy