Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/xlm-roberta-base-banking77-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/xlm-roberta-base-banking77-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickprock/xlm-roberta-base-banking77-classification") model = AutoModelForSequenceClassification.from_pretrained("nickprock/xlm-roberta-base-banking77-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d80668fbfa169586a0de77225595ec160846cb99fc4c785d7bb3a68104aa0433
- Size of remote file:
- 1.11 GB
- SHA256:
- f0de21bc09a58e577491e2adea7724001d8df71d5ba0b430d685d1bf866bc191
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