Instructions to use karthik19967829/XLM-R-es-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karthik19967829/XLM-R-es-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="karthik19967829/XLM-R-es-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("karthik19967829/XLM-R-es-model") model = AutoModelForTokenClassification.from_pretrained("karthik19967829/XLM-R-es-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d691c98986bd0987f58cf7ac41777ee23e50f288de4c9652f91951a4125b6cd
- Size of remote file:
- 1.11 GB
- SHA256:
- 681e040d589b6749af7a3b0aac5efcafa7326aa9ad9903ab46e3e78735cbc7c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.