Text Classification
Transformers
TensorFlow
camembert
generated_from_keras_callback
text-embeddings-inference
Instructions to use joesaa/camembert-sentiment-allocine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joesaa/camembert-sentiment-allocine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joesaa/camembert-sentiment-allocine")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joesaa/camembert-sentiment-allocine") model = AutoModelForSequenceClassification.from_pretrained("joesaa/camembert-sentiment-allocine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
camembert-sentiment-allocine
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: {'name': 'Adam', 'learning_rate': np.float32(0.001), 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.47.0
- TensorFlow 2.18.0
- Datasets 3.1.0
- Tokenizers 0.21.0
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Model tree for joesaa/camembert-sentiment-allocine
Base model
almanach/camembert-base