Instructions to use liy140/bert-base-uncased_relevance_extractor_secondary_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liy140/bert-base-uncased_relevance_extractor_secondary_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liy140/bert-base-uncased_relevance_extractor_secondary_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liy140/bert-base-uncased_relevance_extractor_secondary_binary") model = AutoModelForSequenceClassification.from_pretrained("liy140/bert-base-uncased_relevance_extractor_secondary_binary") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:64ac208e6595acb586c6f068fdc7a6297460325c6359c301d4d3126b6f24a21f
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size 437958648
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