Instructions to use textattack/facebook-bart-large-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/facebook-bart-large-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/facebook-bart-large-RTE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/facebook-bart-large-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/facebook-bart-large-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results_rte.txt from textattack/facebook-bart-large-RTE: direct link, hf CLI and curl.
- Browser
- Download file 72 Bytes
-
https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/eval_results_rte.txt
- Command line
-
hf download hf://textattack/facebook-bart-large-RTE/eval_results_rte.txt
-
curl -L -o eval_results_rte.txt https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/eval_results_rte.txt
72 Bytes
| eval_loss = 0.640379820846849 | |
| eval_acc = 0.8628158844765343 | |
| epoch = 3.0 | |