Instructions to use ModelTC/bart-base-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-stsb") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 507d81a651285e2b946819d543c94fa7f8ac41e7bcd288c4e9cefcd365cb6593
- Size of remote file:
- 623 Bytes
- SHA256:
- 79950a7159c6d3a9cd6eec9a5ac2033ed241ce5232767a3d19da65905dd7ba15
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.