Instructions to use naver/trecdl22-crossencoder-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver/trecdl22-crossencoder-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="naver/trecdl22-crossencoder-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("naver/trecdl22-crossencoder-roberta") model = AutoModelForSequenceClassification.from_pretrained("naver/trecdl22-crossencoder-roberta", device_map="auto") - 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:e4c8af9e2c387340d497e7b7f35676300994eba95d90584d3ca699d012b633bb
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size 710774106
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