Instructions to use nagthgr8/qa-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nagthgr8/qa-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nagthgr8/qa-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nagthgr8/qa-bert-base") model = AutoModel.from_pretrained("nagthgr8/qa-bert-base") - Notebooks
- Google Colab
- Kaggle
Initial model push
Browse files- config.json +1 -1
- model.safetensors +2 -2
config.json
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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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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size 437951328
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