Instructions to use HajNemat/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HajNemat/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HajNemat/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HajNemat/dummy-model") model = AutoModelForMaskedLM.from_pretrained("HajNemat/dummy-model") - Notebooks
- Google Colab
- Kaggle
dummy-model
This model is a fine-tuned version of camembert-base 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 4.47.1
- TensorFlow 2.17.1
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for HajNemat/dummy-model
Base model
almanach/camembert-base