Instructions to use raicrits/DistilFEVERit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raicrits/DistilFEVERit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raicrits/DistilFEVERit")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raicrits/DistilFEVERit") model = AutoModelForSequenceClassification.from_pretrained("raicrits/DistilFEVERit") - Notebooks
- Google Colab
- Kaggle
DistilFEVERit
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.37.0
- TensorFlow 2.8.0
- Datasets 2.13.0
- Tokenizers 0.15.1
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