Instructions to use officialamit558/Test_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use officialamit558/Test_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="officialamit558/Test_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("officialamit558/Test_Model") model = AutoModelForMaskedLM.from_pretrained("officialamit558/Test_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Test_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 4.44.2
- TensorFlow 2.17.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for officialamit558/Test_Model
Base model
almanach/camembert-base