Instructions to use TehranNLP-org/xlnet-base-cased-avg-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TehranNLP-org/xlnet-base-cased-avg-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TehranNLP-org/xlnet-base-cased-avg-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TehranNLP-org/xlnet-base-cased-avg-mnli") model = AutoModelForSequenceClassification.from_pretrained("TehranNLP-org/xlnet-base-cased-avg-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f60e5d097dd3f0ed79c57817e04564bfc9fd8fa8781d63407f5943958be5fc19
- Size of remote file:
- 469 MB
- SHA256:
- 2970d3e03c03cdf37689b6411bb542aa0e41707f0186e83e7dfdafcac1717b72
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.