Instructions to use lilouuch/Qa_bert_base_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilouuch/Qa_bert_base_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lilouuch/Qa_bert_base_6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lilouuch/Qa_bert_base_6") model = AutoModelForSequenceClassification.from_pretrained("lilouuch/Qa_bert_base_6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ae829d6e04d071b3cdabfa881dcd0301ca6e4ea6b53cdd3efcd9fe84e8b9427
- Size of remote file:
- 4.86 kB
- SHA256:
- 444c3b49c008798c60c1270d4db3e4521de1af4e1fee65bc5bdecfd23cd6edd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.