Instructions to use TransWiC/bert-large-P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-P")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-P") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-P", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3056780be0a111a63037253bfa0589172433660ca3b27bf06508d72a5555ad78
- Size of remote file:
- 2.66 GB
- SHA256:
- 60cd781faba3cf254dc6444769b00ce0843a58498aba09986d34706f5a08ffc3
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