https://github.com/amanpriyanshu/transformer-text-autoencoder
Transformer Text AutoEncoder: An autoencoder is a type of artificial neural network used to learn efficient encodings of unlabeled data, the same is employed for textual data employing pre-trained models from the hugging-face library.
https://github.com/amanpriyanshu/transformer-text-autoencoder
Last synced: 7 months ago
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Transformer Text AutoEncoder: An autoencoder is a type of artificial neural network used to learn efficient encodings of unlabeled data, the same is employed for textual data employing pre-trained models from the hugging-face library.
- Host: GitHub
- URL: https://github.com/amanpriyanshu/transformer-text-autoencoder
- Owner: AmanPriyanshu
- License: mit
- Created: 2022-06-12T10:25:18.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-17T16:04:21.000Z (over 3 years ago)
- Last Synced: 2025-03-01T18:22:16.793Z (7 months ago)
- Language: Python
- Size: 143 KB
- Stars: 6
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Transformer-Text-AutoEncoder
Transformer Text AutoEncoder: An autoencoder is a type of artificial neural network used to learn efficient encodings of unlabeled data, the same is employed for textual data employing pre-trained models from the hugging-face library.
## Installation:
```console
pip install Transformer-Text-AutoEncoder
```
## Execution:
### Training
```py
from Transformer_Text_AutoEncoder.AutoEncoder import TTAE
def read(path='./Transformer-Text-AutoEncoder/data/FinancialNews.txt'):
with open(path, "r", encoding='utf-8', errors='ignore') as f:
data = [i.strip() for i in f.readlines()]
return data
sentences = read()
print(sentences[:3])
ttae = TTAE(sentences)
ttae.train(100, batch_size=8)
```
### Predictions
```py
predicted_sentence, encoded_vec = ttae.predict("According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing.")
```
returns the `predicted sentence` as well as the `encoded_vec`.
## Cite Work:
```console
@inproceedings{ttae,
title = {Transformer-Text-AutoEncoder},
author = {Aman Priyanshu},
year = {2022},
publisher = {{GitHub}},
url = {https://github.com/AmanPriyanshu/Transformer-Text-AutoEncoder/}
}
```