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transformers1from transformers import ElectraForPreTraining, ElectraTokenizerFast
2import torch
3
4discriminator = ElectraForPreTraining.from_pretrained("aubmindlab/araelectra-base-discriminator")
5tokenizer = ElectraTokenizerFast.from_pretrained("aubmindlab/araelectra-base-discriminator")
6
7sentence = ""
8fake_sentence = ""
9
10fake_tokens = tokenizer.tokenize(fake_sentence)
11fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
12discriminator_outputs = discriminator(fake_inputs)
13predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
14
15[print("%7s" % token, end="") for token in fake_tokens]
16
17[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()]| Model | HuggingFace Model Name | Size (MB/Params) |
|---|---|---|
| AraELECTRA-base-generator | araelectra-base-generator | 227MB/60M |
| AraELECTRA-base-discriminator | araelectra-base-discriminator | 516MB/135M |
| Model | Hardware | num of examples (seq len = 512) | Batch Size | Num of Steps | Time (in days) |
|---|---|---|---|---|---|
| AraELECTRA-base | TPUv3-8 | - | 256 | 2M | 24 |
pip install arabert1from arabert.preprocess import ArabertPreprocessor
2
3model_name="araelectra-base"
4arabert_prep = ArabertPreprocessor(model_name=model_name)
5
6text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري"
7arabert_prep.preprocess(text)
8
9>>> output: ولن نبالغ إذا قلنا : إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروريaubmindlab usernamewget https://huggingface.co/aubmindlab/MODEL_NAME/resolve/main/tf1_model.tar.gz where MODEL_NAME is any model under the aubmindlab name@inproceedings{antoun-etal-2021-araelectra,
title = "{A}ra{ELECTRA}: Pre-Training Text Discriminators for {A}rabic Language Understanding",
author = "Antoun, Wissam and
Baly, Fady and
Hajj, Hazem",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.wanlp-1.20",
pages = "191--195",
}