Instructions to use mascIT/bertina-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mascIT/bertina-3M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mascIT/bertina-3M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mascIT/bertina-3M") model = AutoModelForMaskedLM.from_pretrained("mascIT/bertina-3M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForMaskedLM
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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{
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"_name_or_path": "../data/exp-bertina-3M/
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"architectures": [
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"BertForMaskedLM"
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],
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{
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"_name_or_path": "../data/exp-bertina-3M/6/checkpoint-10024",
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"architectures": [
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"BertForMaskedLM"
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],
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:06cca117bdafe3277d1001561ed35e3fb22e68a080cf112d0a949a972a29f4c6
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size 12052336
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