Instructions to use negfir/Bertbase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use negfir/Bertbase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="negfir/Bertbase")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("negfir/Bertbase") model = AutoModelForMaskedLM.from_pretrained("negfir/Bertbase") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +2 -2
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "bert-base-
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"architectures": [
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"BertForMaskedLM"
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],
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"transformers_version": "4.13.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size":
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}
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{
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"_name_or_path": "bert-base-cased",
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"architectures": [
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"BertForMaskedLM"
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],
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"transformers_version": "4.13.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:7dbb57161cedfbe95ae5fe9caa58db429e7db0aae25b91dc927314a040b20b87
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size 433448043
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