Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use azamat/geocoder_coordinates_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use azamat/geocoder_coordinates_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="azamat/geocoder_coordinates_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("azamat/geocoder_coordinates_model") model = AutoModelForSequenceClassification.from_pretrained("azamat/geocoder_coordinates_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 087bb29035dc7507c4633de19a2885d8a9d9a6b5b1b010accf5703912687afca
- Size of remote file:
- 1.11 GB
- SHA256:
- 56f62bb834bb3fe0ca4c8603494b6442e37cea24e2680c51d7568098348e3b13
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