Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use azamat/geocoder_relevancy_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use azamat/geocoder_relevancy_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="azamat/geocoder_relevancy_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("azamat/geocoder_relevancy_model") model = AutoModelForSequenceClassification.from_pretrained("azamat/geocoder_relevancy_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0380997fbd6125641b639635c44041e1ffe026789d7dfd33411f643873c5d1ca
- Size of remote file:
- 711 MB
- SHA256:
- 4156b47fc2881ea3ae016a72a82be52b78744fc0f3b9d32b2ccebaabfb1714c7
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