Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.
- A modern neural network architecture which utilizes new techniques as attention-weighting and skip-embedding to accelerate training and improve model quality.
- Able to train on and generate text at either the character-level or word-level.
- Able to configure RNN size, the number of RNN layers, and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs when trained on the GPU, which massively speeds up training time as opposed to normal LSTM implementations.
- Able to train the model using contextual labels, allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.
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Release files for textgenrnn 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| textgenrnn-2.0.0.tar.gz | 1.7 MB | Details |
Release files / textgenrnn-2.0.0.tar.gz
| Download URL | textgenrnn-2.0.0.tar.gz |
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| Size | 1.7 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
c2b6f1c201c76d5a6021079e95a8db499bbe15d9f3448d33cb51c0cd496c86f8
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twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5
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