Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use maticzav/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maticzav/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maticzav/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maticzav/model") model = AutoModelForSequenceClassification.from_pretrained("maticzav/model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2e7be6a288c9c98de0d350031a1655ce3dac32db4b6f43c4850737943e8c517
- Size of remote file:
- 17.1 MB
- SHA256:
- 1d237c4974cfea16dffbc5035028fea5b47ad8b653af90c908b7a35bdc8b0d90
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