Instructions to use mschwab/va_bert_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mschwab/va_bert_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mschwab/va_bert_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mschwab/va_bert_classification") model = AutoModelForSequenceClassification.from_pretrained("mschwab/va_bert_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebb5babbc42bb8c9033a04cc356b1d7760a9fb477785bc465d7aafd0f57da344
- Size of remote file:
- 2.29 kB
- SHA256:
- 7d2b3ace07fede7de6d43751f1e65cda8303a9247ba44e43d37b3ca0ae11287d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.