Stanza
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages (by stanfordnlp)
flair
A very simple framework for state-of-the-art Natural Language Processing (NLP) (by flairNLP)
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Stanza | flair | |
---|---|---|
8 | 9 | |
7,047 | 13,566 | |
1.1% | 1.0% | |
9.8 | 9.4 | |
1 day ago | 2 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
Stanza
Posts with mentions or reviews of Stanza.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-06.
- Down and Out in the Magic Kingdom
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Parts of speech tagged for German
I use Python's spacy library: https://spacy.io/models/de or stanza: https://stanfordnlp.github.io/stanza/ each with their respective language models.
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Off the shelf sentence parsers?
stanza has a constituency parser. There's a model compatible with the dev branch with an accuracy of 95.8 on PTB, using Roberta as a bottom layer, so it's pretty decent at this point. (The currently released model is not as accurate, but it's easy to get the better model to you.) There's also Tregex as a Java addon which can very easily search for a noun phrase highest up in the tree: NP !>> NP will search for a noun phrase which is not dominated by any higher up noun phrase.
- The Spacy NER model for Spanish is terrible
- Spacy vs NLTK for Spanish Language Statistical Tasks
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Stanza not tokenising sentences as expected
I am using Stanza to tokenise the sentences:
- Stanza β A Python NLP Package for Many Human Languages
flair
Posts with mentions or reviews of flair.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-22.
- Flair: A simple framework for state-of-the-art Natural Language Processing
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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
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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
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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.
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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
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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...
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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.
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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.
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[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.
What are some alternatives?
When comparing Stanza and flair you can also consider the following projects:
spaCy - π« Industrial-strength Natural Language Processing (NLP) in Python
spacy-models - π« Models for the spaCy Natural Language Processing (NLP) library
NLTK - NLTK Source
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
Jieba - η»ε·΄δΈζεθ―
gensim - Topic Modelling for Humans
pytext - A natural language modeling framework based on PyTorch
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.