Instructions to use karthik19967829/XLM-R-lt-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karthik19967829/XLM-R-lt-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="karthik19967829/XLM-R-lt-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("karthik19967829/XLM-R-lt-model") model = AutoModelForTokenClassification.from_pretrained("karthik19967829/XLM-R-lt-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69a4a2d287c6d2d4adbf7b97ec9cfbb0551380650569ff4c4814314a07f9d262
- Size of remote file:
- 2.93 kB
- SHA256:
- 09e828936a69d1efcecea80f8c65507252dd6494329b8fc7f07bf5b2c5daf237
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.