https://github.com/kaushaltrivedi/fast-bert This repository contains the Jupyter notebook for multilabel text classification using BERT. This is the accompanying code for the medium story https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d. ...
We often encounter classic classification tasks such as binary classification (two labels) and multiclass classification (more than two labels). In this case, we would train the classifier, and the model would try to predict one of the labels from all the available labels. The dataset used for...
This library is based on theTransformerslibrary by HuggingFace. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize a model, train the model, and evaluate a model. Currently supports Sequence Classification, Token Classification (NE...
DeBERTa is a model architecture that provides disentangled attention in addition to an enhanced mask decoder compared to BERT and RoBERTa. This massive 1.5-parameter pre-trained language model is being funded by Microsoft [44]. In the context of Arabic multi-label question classification, the morpho...
Evaluation of emotion classification schemes in social media text: An annotation-based approach. BMC Psychol. 2024, 12, 503. [Google Scholar] [CrossRef] [PubMed] Hugging Face. The AI Community Building the Future. Datasets. Available online: https://huggingface.co/datasets (accessed on 24 ...
The learned token node representations 𝑊(1)W(1) from the second GCN layer are extracted and used as the initial weights for fine-tuning a pre-trained BERT on the same multi-label emotion classification task using the last hidden layer of the [CLS] token. Similarly, we use the binary ...
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An Open-source Neural Hierarchical Multi-label Text Classification Toolkit - Tencent/NeuralNLP-NeuralClassifier
This library is based on the Transformers library by HuggingFace. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize a model, train the model, and evaluate a model. Currently supports Sequence Classification, Token Classification ...
(http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip), bert-base-uncased.tar.gz(https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz) and vocab.txt(https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt) with the corresponding ...