Instructions to use diptanu/fBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diptanu/fBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="diptanu/fBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("diptanu/fBERT") model = AutoModelForMaskedLM.from_pretrained("diptanu/fBERT") - Notebooks
- Google Colab
- Kaggle
Update config.json
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config.json
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"
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"architectures": [
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"BertForMaskedLM"
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{
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"name_or_path": "bert-base-uncased",
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"architectures": [
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"BertForMaskedLM"
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],
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