v0.31.9

0.31.9 (2022-09-24)

new:

• N/A

changed

• N/A

fixed:

• Adjustment for kwe
• Fixed problem with importing ktrain without TensorFlow installed

v0.31.8

0.31.8 (2022-09-08)

new:

• N/A

changed

• N/A

fixed:

• Fixed paragraph tokenization in AnswerExtractor

v0.31.7

0.31.7 (2022-08-04)

new:

• N/A

changed

• re-arranged dep warnings for TF
• ktrain now pinned to transformers==4.17.0. Python 3.6 users can downgrade to transformers==4.10.3 and still use ktrain.

fixed:

• N/A

v0.31.6

0.31.6 (2022-08-02)

new:

• N/A

changed

• updated dependencies to work with newer versions (but temporarily continue pinning to transformers==4.10.1)

fixed:

• fixes for newer networkx

v0.31.5

0.31.5 (2022-08-01)

new:

• N/A

changed

• N/A

fixed:

• fix release

v0.31.4

0.31.4 (2022-08-01)

new:

• N/A

changed

• TextPredictor.explain and ImagePredictor.explain now use a different fork of eli5: pip install https://github.com/amaiya/eli5-tf/archive/refs/heads/master.zip

fixed:

• Fixed loss_fn_from_model function to work with DISABLE_V2_BEHAVIOR properly
• TextPredictor.explain and ImagePredictor.explain now work with tensorflow>=2.9 and scipy>=1.9 (due to new eli5-tf fork -- see above)

v0.31.3

0.31.3 (2022-07-15)

new:

• N/A

changed

• added alnum check and period check to KeywordExtractor

fixed:

• fixed bug in text.qa.core caused by previous refactoring of paragraph_tokenize and tokenize

v0.31.2

0.31.2 (2022-05-20)

new:

• N/A

changed

• added truncate_to argument (default:5000) and minchars argument (default:3) argument to KeywordExtractor.extract_keywords method.
• added score_by argument to KeywordExtractor.extract_keywords. Default is freqpos, which means keywords are now ranked by a combination of frequency and position in document.

fixed:

• N/A

v0.31.1

0.31.1 (2022-05-17)

new:

• N/A

changed

• Allow for returning prediction probabilities when merging tokens in sequence-tagging (PR #445)
• added basic ML pipeline test to workflow using latest TensorFlow

fixed:

• N/A

v0.31.0

0.31.0 (2022-05-07)

new:

• The text.ner.models.sequence_tagger now supports word embeddings from non-BERT transformer models (e.g., roberta-base, codebert). Thanks to @Niekvdplas.
• Custom tokenization can now be used in sequence-tagging even when using transformer word embeddings. See custom_tokenizer argument to NERPredictor.predict.

changed

• [breaking change] In the text.ner.models.sequence_tagger function, the bilstm-bert model is now called bilstm-transformer and the bert_model parameter has been renamed to transformer_model.
• [breaking change] The syntok package is now used as the default tokenizer for NERPredictor (sequence-tagging prediction). To use the tokenization scheme from older versions of ktrain, you can import the re and string packages and supply this function to the custom_tokenizer argument: lambda s: re.compile(f"([{string.punctuation}“”¨«»®´·º½¾¿¡§£₤‘’])").sub(r" \1 ", s).split().
• Code base was reformatted using black and isort
• ktrain now supports TIKA for text extraction in the text.textractor.TextExtractor package with the use_tika=True argument as default. To use the old-style text extraction based on the textract package, you can supply use_tika=False to TextExtractor.
• removed warning about sentence pair classification to avoid confusion

fixed:

• N/A