camembert-base-finetuned-text-classification

RubenBueno/camembert-base-finetuned-text-classification

camembert-base-finetuned-text-classification is a feature extraction model from RubenBueno. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.

probably proofread and complete it, then remove this comment. --

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At a glance

Task: Feature Extraction
Library: transformers
License: mit
Model type: camembert
Access: Public
Created: May 13, 2025
Updated: May 16, 2025
SHA: 8a53986e

Base models

Model card

From the Hugging Face model README

RubenBueno/camembert-base-finetuned-text-classification

This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.3200
Validation Loss: 0.2337
Train Weighted acc: tf.Tensor(0.98794097, shape=(), dtype=float32)
Train Accuracy: 0.9780
Train F1: 0.9788
Epoch: 14

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 2040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Weighted acc	Train Accuracy	Train F1	Epoch
4.3328	3.7022	tf.Tensor(0.5895147, shape=(), dtype=float32)	0.8956	0.8965	0
3.5969	3.4171	tf.Tensor(0.6352869, shape=(), dtype=float32)	0.8010	0.8365	1
3.2404	2.8696	tf.Tensor(0.72062576, shape=(), dtype=float32)	0.9114	0.9123	2
2.7348	2.1050	tf.Tensor(0.8133777, shape=(), dtype=float32)	0.9215	0.9270	3
2.2544	1.6001	tf.Tensor(0.8648611, shape=(), dtype=float32)	0.9221	0.9299	4
1.8183	1.2205	tf.Tensor(0.9124412, shape=(), dtype=float32)	0.9334	0.9394	5
1.4753	0.9544	tf.Tensor(0.9317861, shape=(), dtype=float32)	0.9518	0.9554	6
1.0343	0.7026	tf.Tensor(0.95245564, shape=(), dtype=float32)	0.9596	0.9620	7
0.8071	0.5026	tf.Tensor(0.9712267, shape=(), dtype=float32)	0.9688	0.9702	8
0.6989	0.4500	tf.Tensor(0.9746262, shape=(), dtype=float32)	0.9697	0.9711	9
0.5615	0.3510	tf.Tensor(0.9816495, shape=(), dtype=float32)	0.9708	0.9722	10
0.4585	0.3062	tf.Tensor(0.9840923, shape=(), dtype=float32)	0.9748	0.9757	11
0.3982	0.2611	tf.Tensor(0.98653185, shape=(), dtype=float32)	0.9753	0.9762	12
0.3399	0.2452	tf.Tensor(0.98775315, shape=(), dtype=float32)	0.9783	0.9790	13
0.3200	0.2337	tf.Tensor(0.98794097, shape=(), dtype=float32)	0.9780	0.9788	14

Framework versions

Transformers 4.50.3
TensorFlow 2.19.0
Datasets 3.6.0
Tokenizers 0.21.1

Task: Feature Extraction
Library: transformers
Type: camembert
License: mit
Created: May 13, 2025
Updated: May 16, 2025