Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
Arabic
bert
Arabic
Dialect
Egyptian
Gulf
Levantine
Classical Arabic
MSA
Modern Standard Arabic
Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/bert-base-arabic-camelbert-mix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAMeL-Lab/bert-base-arabic-camelbert-mix")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-mix") model = AutoModelForMaskedLM.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-mix") - Inference
- Notebooks
- Google Colab
- Kaggle
Go Inoue commited on
Commit ·
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Parent(s): d953ee1
Fix typo
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README.md
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@@ -120,7 +120,7 @@ We follow the original English BERT model's hyperparameters for pre-training, un
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- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
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- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
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- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
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- We use \\(F_{1}
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- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
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### Results
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- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
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- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
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- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
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- We use \\(F_{1}\\) score as a metric for all tasks.
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- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
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### Results
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