Instructions to use livinNector/tabert-2k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use livinNector/tabert-2k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="livinNector/tabert-2k")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("livinNector/tabert-2k") model = AutoModelForMaskedLM.from_pretrained("livinNector/tabert-2k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a0b3dc1c06c171c86ef1ca6b3e46637ce4690a9b01ec25cbb565624ce9a864e
- Size of remote file:
- 180 MB
- SHA256:
- f56cc04b28096096105621d5f6e07cbefefe61c41f3bc27f5c4fa5d693cc7154
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