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Update README.md and config.json, and add aragpt2-large model to ARAGPT2_PRETRAINED_MODEL_ARCHIVE_LIST
Browse files- README.md +16 -13
- config.json +2 -1
- modeling_aragpt2.py +1 -0
README.md
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@@ -6,9 +6,9 @@ license_link: https://github.com/aub-mind/arabert/blob/master/aragpt2/LICENSE
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datasets:
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- wikipedia
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- Osian
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- oscar
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- Assafir
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inference: false
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widget:
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- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
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# Arabic GPT2
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<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
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You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
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AraGPT2 is trained on the same large Arabic Dataset as AraBERTv2.
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## Testing the model using `transformers`:
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```python
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from transformers import
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#for base and medium
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from transformers import GPT2LMHeadModel
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#for large and mega
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from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
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from arabert.preprocess import ArabertPreprocessor
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text=""
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text_clean = arabert_prep.preprocess(text)
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model =
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tokenizer = GPT2TokenizerFast.from_pretrained(MODEL_NAME)
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generation_pipeline = pipeline(
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#feel free to try different decoding settings
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generation_pipeline(text,
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pad_token_id=tokenizer.eos_token_id,
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num_beams=10,
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max_length=200,
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top_p=0.9,
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datasets:
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- wikipedia
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- Osian
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- arabic-billion-words
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- oscar
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- Assafir-private
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inference: false
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widget:
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- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
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# Arabic GPT2
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<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
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You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
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AraGPT2 is trained on the same large Arabic Dataset as AraBERTv2.
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# NOTE: The model expects the input to be preprocessed using the `arabert` library.
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if not the model won't be able to generate the correct output.
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## Testing the model using `transformers`:
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The model code is now hosted on HuggingFace so you need to use the `trust_remote_code` flag, and can be used as follows:
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```python
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from transformers import AutoModelForCausalLM, pipeline
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from arabert.preprocess import ArabertPreprocessor
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text=""
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text_clean = arabert_prep.preprocess(text)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
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tokenizer = GPT2TokenizerFast.from_pretrained(MODEL_NAME)
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generation_pipeline = pipeline(
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"text-generation", model=MODEL_NAME, trust_remote_code=True
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)
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#feel free to try different decoding settings
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generation_pipeline(text,
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pad_token_id=pipeline.tokenizer.eos_token_id,
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num_beams=10,
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max_length=200,
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top_p=0.9,
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config.json
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"no_repeat_ngram_size": 3
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}
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},
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"vocab_size": 64000
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}
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"no_repeat_ngram_size": 3
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}
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},
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"vocab_size": 64000,
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"tokenizer_class": "GPT2Tokenizer"
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}
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modeling_aragpt2.py
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_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
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ARAGPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"aubmindlab/aragpt2-mega",
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# See all AraGPT2 models at https://huggingface.co/models?filter=aragpt2
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]
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_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
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ARAGPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"aubmindlab/aragpt2-large",
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"aubmindlab/aragpt2-mega",
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# See all AraGPT2 models at https://huggingface.co/models?filter=aragpt2
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]
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