Token Classification
Transformers
PyTorch
roberta
LABEL-0 = NONE
LABEL-1 = B-DATE
LABEL-2 = I-DATE
LABEL-3 = B-TIME
LABEL-4 = I-TIME
LABEL-5 = B-DURATION
LABEL-6 = I-DURATION
LABEL-7 = B-SET
LABEL-8 = I-SET
Instructions to use asdc/Bio-RoBERTime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asdc/Bio-RoBERTime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="asdc/Bio-RoBERTime")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asdc/Bio-RoBERTime") model = AutoModelForTokenClassification.from_pretrained("asdc/Bio-RoBERTime") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
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:c7023df3a06662c9f0b3dd21ac726f191012c5b428b9c0895cf96f5660b69115
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size 501605900
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