flair
seqeval
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flair | seqeval | |
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9 | 1 | |
13,487 | 1,039 | |
0.9% | 1.7% | |
9.4 | 0.0 | |
6 days ago | about 2 months 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
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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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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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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.
seqeval
We haven't tracked posts mentioning seqeval yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
spacy-models - 💫 Models for the spaCy Natural Language Processing (NLP) library
scikit-learn - scikit-learn: machine learning in Python
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
gensim - Topic Modelling for Humans
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.