Instructions to use VCNC/bert_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VCNC/bert_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VCNC/bert_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VCNC/bert_3") model = AutoModelForSequenceClassification.from_pretrained("VCNC/bert_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be2ea32e28e39c5e18e54bf1cbb41baf6651216f8444dcf810fbf73a1292214d
- Size of remote file:
- 692 MB
- SHA256:
- c5b187965366ff58e56adac789e24a663e74d1e6561e074502db04852a523268
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.