--- license: mit base_model: camembert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: VogagenRelation results: [] --- # VogagenRelation This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6932 - Accuracy: 0.5059 ## 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: - learning_rate: 1e-08 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.21 | 100 | 0.6932 | 0.5043 | | No log | 0.42 | 200 | 0.6932 | 0.5051 | | No log | 0.62 | 300 | 0.6932 | 0.5051 | | No log | 0.83 | 400 | 0.6932 | 0.5051 | | 0.693 | 1.04 | 500 | 0.6932 | 0.5051 | | 0.693 | 1.25 | 600 | 0.6932 | 0.5051 | | 0.693 | 1.46 | 700 | 0.6932 | 0.5043 | | 0.693 | 1.66 | 800 | 0.6932 | 0.5043 | | 0.693 | 1.87 | 900 | 0.6932 | 0.5051 | | 0.693 | 2.08 | 1000 | 0.6932 | 0.5043 | | 0.693 | 2.29 | 1100 | 0.6932 | 0.5043 | | 0.693 | 2.49 | 1200 | 0.6932 | 0.5043 | | 0.693 | 2.7 | 1300 | 0.6932 | 0.5043 | | 0.693 | 2.91 | 1400 | 0.6932 | 0.5043 | | 0.6938 | 3.12 | 1500 | 0.6932 | 0.5043 | | 0.6938 | 3.33 | 1600 | 0.6932 | 0.5043 | | 0.6938 | 3.53 | 1700 | 0.6932 | 0.5043 | | 0.6938 | 3.74 | 1800 | 0.6932 | 0.5043 | | 0.6938 | 3.95 | 1900 | 0.6932 | 0.5043 | | 0.693 | 4.16 | 2000 | 0.6932 | 0.5051 | | 0.693 | 4.37 | 2100 | 0.6932 | 0.5051 | | 0.693 | 4.57 | 2200 | 0.6932 | 0.5051 | | 0.693 | 4.78 | 2300 | 0.6932 | 0.5051 | | 0.693 | 4.99 | 2400 | 0.6932 | 0.5051 | | 0.6927 | 5.2 | 2500 | 0.6932 | 0.5051 | | 0.6927 | 5.41 | 2600 | 0.6932 | 0.5051 | | 0.6927 | 5.61 | 2700 | 0.6932 | 0.5051 | | 0.6927 | 5.82 | 2800 | 0.6932 | 0.5051 | | 0.6927 | 6.03 | 2900 | 0.6932 | 0.5051 | | 0.6927 | 6.24 | 3000 | 0.6932 | 0.5051 | | 0.6927 | 6.44 | 3100 | 0.6932 | 0.5059 | | 0.6927 | 6.65 | 3200 | 0.6932 | 0.5059 | | 0.6927 | 6.86 | 3300 | 0.6932 | 0.5059 | | 0.6927 | 7.07 | 3400 | 0.6932 | 0.5059 | | 0.6934 | 7.28 | 3500 | 0.6932 | 0.5059 | | 0.6934 | 7.48 | 3600 | 0.6932 | 0.5059 | | 0.6934 | 7.69 | 3700 | 0.6932 | 0.5059 | | 0.6934 | 7.9 | 3800 | 0.6932 | 0.5059 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1