Instructions to use Akshayextreme/SemEval_2015_PIT_crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akshayextreme/SemEval_2015_PIT_crossencoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Akshayextreme/SemEval_2015_PIT_crossencoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Akshayextreme/SemEval_2015_PIT_crossencoder") model = AutoModelForSequenceClassification.from_pretrained("Akshayextreme/SemEval_2015_PIT_crossencoder") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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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:cb2e49e810ae0305c5457a642d5ce17748d14c2d441aac4c2e9767fdafc29d8c
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size 328493404
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