Instructions to use oe2015/XLMsubtask2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oe2015/XLMsubtask2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oe2015/XLMsubtask2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oe2015/XLMsubtask2") model = AutoModelForSequenceClassification.from_pretrained("oe2015/XLMsubtask2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0be263f5552ad2226c49f0980abaee7065c096e333adf59607bb276bd9f52026
- Size of remote file:
- 1.11 GB
- SHA256:
- b5cc1878ed8f809a4a068602fabc2e0b0965a60742eef43e66f0de6c72f7a348
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