Instructions to use stvnl/rm_bert_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stvnl/rm_bert_en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stvnl/rm_bert_en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stvnl/rm_bert_en") model = AutoModelForSequenceClassification.from_pretrained("stvnl/rm_bert_en") - 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 sha256:9ce1874119d1e0f43276da7e0a9b775ec73607bf2a08e2b3a23ef19f90c13810
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size 437955572
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