Instructions to use kvsr/merged-model-sequence-classification-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kvsr/merged-model-sequence-classification-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kvsr/merged-model-sequence-classification-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kvsr/merged-model-sequence-classification-binary") model = AutoModelForSequenceClassification.from_pretrained("kvsr/merged-model-sequence-classification-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 447005c6cbbcdc1d850e8f831f8f00f81ecd069c83deb8cfa89050c04576a5f9
- Size of remote file:
- 134 MB
- SHA256:
- c225c1a220de45bdc9fdc2f4eb24285a23f5498df67a6bb9a97f07b9978f813b
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