Instructions to use NasimB/bert-concat-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-concat-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-concat-3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-concat-3") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-concat-3") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- generation_config.json +5 -0
- pytorch_model.bin +1 -1
generation_config.json
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{
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"_from_model_config": true,
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"pad_token_id": 0,
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"transformers_version": "4.26.1"
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:bc4deac0264cfb37840691738d0b30e3510e1226ff9f53c7b50753b8c0146c0e
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size 438128811
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