# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("smend0/notebook-test")
model = AutoModelForMaskedLM.from_pretrained("smend0/notebook-test", device_map="auto")Quick Links
notebook-test
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 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for smend0/notebook-test
Base model
almanach/camembert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="smend0/notebook-test")