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
Commit ·
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README.md
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# QARiB: QCRI Arabic and Dialectal BERT
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## About QARiB
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|**Named-Entity Recognition (NER) | 49.38% | 64.97% | 66.63% | 64.04% | 61.62% |
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|**Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | 91.94% |
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|**Sentiment Analysis | 86.61% | 90.80% | 93.58% | 83.27% | 93.31% |
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## Model Weights and Vocab Download
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}
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```
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---
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language: ar
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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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- twitter
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metrics:
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- f1
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widget:
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- text: " شو عندكم يا [MASK] ."
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---
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# QARiB: QCRI Arabic and Dialectal BERT
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## About QARiB
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|**Named-Entity Recognition (NER) | 49.38% | 64.97% | 66.63% | 64.04% | 61.62% |
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|**Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | 91.94% |
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|**Sentiment Analysis | 86.61% | 90.80% | 93.58% | 83.27% | 93.31% |
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## Model Weights and Vocab Download
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}
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```
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