Instructions to use TehranNLP/xlnet-base-cased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TehranNLP/xlnet-base-cased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TehranNLP/xlnet-base-cased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TehranNLP/xlnet-base-cased-mnli") model = AutoModelForSequenceClassification.from_pretrained("TehranNLP/xlnet-base-cased-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 174dbe89b2b815383cd73727d039c134000df94bf5da1dfa1283879ca0e020d9
- Size of remote file:
- 469 MB
- SHA256:
- a93c4528e1defbdb1df41d0bcb547ed6ef06c47e09336c3a3c62831978df972c
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