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amaiya committed Apr 17, 2020
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### News and Announcements
- **2020-04-15:**
- ***ktrain*** **v0.14.x is released** and now includes support for **open-domain question-answering**. See the [example QA notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/question_answering_with_bert.ipynb)
- ***ktrain*** **v0.14.x is released** and now includes support for **open-domain question-answering**. See the [example QA notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/question_answering_with_bert.ipynb)
- **2020-04-09:**
- ***ktrain*** **v0.13.x is released** and includes support for:
- **link prediction** using graph neural networks - [see example link prediction notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/graphs/cora_link_prediction-GraphSAGE.ipynb) on citation prediction
- **text summarization** with pretrained BART - [see example summarization notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/text_summarization_with_bart.ipynb) <sub><sup>(Summarization included in v0.13.1, but not v0.13.0.)</sup></sub>
- **link prediction** using graph neural networks - [see example link prediction notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/graphs/cora_link_prediction-GraphSAGE.ipynb) on citation prediction
- **text summarization** with pretrained BART - [see example summarization notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_summarization_with_bart.ipynb) <sub><sup>(Summarization included in v0.13.1, but not v0.13.0.)</sup></sub>
```python
# text summarization with BART
from ktrain import text
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- **2020-03-31:**
- ***ktrain*** **v0.12.x is released** and now includes BERT embeddings (i.e., BERT, DistilBert, and Albert) that can be used for downstream tasks like building sequence-taggers (i.e., NER)
for any language such as English, Chinese, Russian, Arabic, Dutch, etc. See [this English NER example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/CoNLL2003-BiLSTM.ipynb) or the [Dutch NER notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb) for examples on how to use this feature.
for any language such as English, Chinese, Russian, Arabic, Dutch, etc. See [this English NER example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/CoNLL2003-BiLSTM.ipynb) or the [Dutch NER notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb) for examples on how to use this feature.
*ktrain* also supports NER with domain-specific embeddings from [community-uploaded Hugging Face models](https://huggingface.co/models) such as [BioBERT](https://arxiv.org/abs/1901.08746) for the biomedical domain:
```python
# NER with BioBERT embeddings
Expand All @@ -45,18 +45,18 @@ learner.fit(0.01, 1, cycle_len=5)
- `text` data:
- **Text Classification**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), [NBSVM](https://www.aclweb.org/anthology/P12-2018), [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/IMDb-BERT.ipynb)]</sup></sub>
- **Text Regression**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), Embedding-based linear text regression, [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_regression_example.ipynb)]</sup></sub>
- **Sequence Labeling (NER)**: Bidirectional LSTM with optional [CRF layer](https://arxiv.org/abs/1603.01360) and various embedding schemes such as pretrained [BERT](https://huggingface.co/transformers/pretrained_models.html) and [fasttext](https://fasttext.cc/docs/en/crawl-vectors.html) word embeddings and character embeddings <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb)]</sup></sub>
- **Sequence Labeling (NER)**: Bidirectional LSTM with optional [CRF layer](https://arxiv.org/abs/1603.01360) and various embedding schemes such as pretrained [BERT](https://huggingface.co/transformers/pretrained_models.html) and [fasttext](https://fasttext.cc/docs/en/crawl-vectors.html) word embeddings and character embeddings <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb)]</sup></sub>
- **Ready-to-Use NER models for English, Chinese, and Russian** with no training required <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/shallownlp-examples.ipynb)]</sup></sub>
- **Unsupervised Topic Modeling** with [LDA](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-topic_modeling.ipynb)]</sup></sub>
- **Document Similarity with One-Class Learning**: given some documents of interest, find and score new documents that are semantically similar to them using [One-Class Text Classification](https://en.wikipedia.org/wiki/One-class_classification) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-document_similarity_scorer.ipynb)]</sup></sub>
- **Document Recommendation Engine**: given text from a sample document, recommend documents that are thematically-related to it from a larger corpus <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-recommendation_engine.ipynb)]</sup></sub>
- **Text Summarization**: summarize long documents with a pretrained BART model - no training required <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/text_summarization_with_bart.ipynb)]</sup></sub>
- **Open-Domain Question-Answering**: ask a large text corpus questions and receive exact answers <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/question_answering_with_bert.ipynb)]</sup></sub>
- **Text Summarization**: summarize long documents with a pretrained BART model - no training required <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_summarization_with_bart.ipynb)]</sup></sub>
- **Open-Domain Question-Answering**: ask a large text corpus questions and receive exact answers <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/question_answering_with_bert.ipynb)]</sup></sub>
- `vision` data:
- **image classification** (e.g., [ResNet](https://arxiv.org/abs/1512.03385), [Wide ResNet](https://arxiv.org/abs/1605.07146), [Inception](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/vision/dogs_vs_cats-ResNet50.ipynb)]</sup></sub>
- `graph` data:
- **node classification** with graph neural networks ([GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/graphs/pubmed_node_classification-GraphSAGE.ipynb)]</sup></sub>
- **link prediction** with graph neural networks ([GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/graphs/cora_link_prediction-GraphSAGE.ipynb)]</sup></sub>
- **link prediction** with graph neural networks ([GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/graphs/cora_link_prediction-GraphSAGE.ipynb)]</sup></sub>
- estimate an optimal learning rate for your model given your data using a Learning Rate Finder
- utilize learning rate schedules such as the [triangular policy](https://arxiv.org/abs/1506.01186), the [1cycle policy](https://arxiv.org/abs/1803.09820), and [SGDR](https://arxiv.org/abs/1608.03983) to effectively minimize loss and improve generalization
- build text classifiers for any language (e.g., [Chinese Sentiment Analysis with BERT](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/ChineseHotelReviews-BERT.ipynb), [Arabic Sentiment Analysis with NBSVM](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/ArabicHotelReviews-nbsvm.ipynb))
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