flair
hp-dataset
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| flair | hp-dataset | |
|---|---|---|
| 9 | 1 | |
| 13,558 | 1 | |
| 0.8% | - | |
| 9.4 | 10.0 | |
| about 17 hours ago | over 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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flair
- 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.
hp-dataset
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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 dataset used is this: https://github.com/Kornflex28/hp-dataset/tree/main/datasets
What are some alternatives?
spacy-models - 💫 Models for the spaCy Natural Language Processing (NLP) library
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
gensim - Topic Modelling for Humans
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
MAX-Toxic-Comment-Classifier - Detect 6 types of toxicity in user comments.
empirist-corpus - A web and social media corpus based on the dataset of the EmpiriST 2015 shared task
NLP-progress - Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
PURE - [NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !