Feature Extraction
Transformers
PyTorch
Safetensors
multitask_modernbert
Generated from Trainer
custom_code
Instructions to use SociauxLing/modernbert-CGEdit-AAE_cg_d3_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SociauxLing/modernbert-CGEdit-AAE_cg_d3_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SociauxLing/modernbert-CGEdit-AAE_cg_d3_final", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SociauxLing/modernbert-CGEdit-AAE_cg_d3_final", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
modernbert-CGEdit-AAE_cg_d3_final
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8940
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.6175 | 1.0 | 183 | 0.9031 |
| 3.5826 | 2.0 | 366 | 0.8996 |
| 3.5815 | 3.0 | 549 | 0.8981 |
| 3.6007 | 4.0 | 732 | 0.8983 |
| 3.5566 | 5.0 | 915 | 0.8978 |
| 3.5408 | 6.0 | 1098 | 0.8958 |
| 3.5225 | 7.0 | 1281 | 0.8953 |
| 3.5437 | 8.0 | 1464 | 0.8945 |
| 3.5038 | 9.0 | 1647 | 0.8939 |
| 3.3342 | 10.0 | 1830 | 0.8945 |
| 3.5208 | 11.0 | 2013 | 0.8948 |
| 3.5108 | 12.0 | 2196 | 0.8938 |
| 3.5158 | 13.0 | 2379 | 0.8936 |
| 3.5038 | 14.0 | 2562 | 0.8942 |
| 3.4915 | 15.0 | 2745 | 0.8938 |
| 3.4983 | 16.0 | 2928 | 0.8938 |
| 3.4949 | 17.0 | 3111 | 0.8939 |
| 3.5022 | 18.0 | 3294 | 0.8940 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.5.1+cu121
- Tokenizers 0.22.1
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