Instructions to use ahmedabdelali/bert-base-qarib with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedabdelali/bert-base-qarib with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ahmedabdelali/bert-base-qarib")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ahmedabdelali/bert-base-qarib") model = AutoModelForMaskedLM.from_pretrained("ahmedabdelali/bert-base-qarib", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Ahmed Abdelali commited on
Commit ·
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Parent(s): 6c37233
pushd updates for README and config
Browse files- README.md +4 -2
- config.json +1 -0
README.md
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tags:
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- pytorch
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- tf
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datasets:
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- arabic_billion_words
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- open_subtitles
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- Number of Layers: 12
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## Training QARiB
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## Using QARiB
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](./Using_QARiB.md)
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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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tags:
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- pytorch
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- tf
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- QARiB
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- qarib
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datasets:
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- arabic_billion_words
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- open_subtitles
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- Number of Layers: 12
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## Training QARiB
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See details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)
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## Using QARiB
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)
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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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config.json
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"model_type": "bert",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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