Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use cvapict/distilbert-base-multilingual-cased-aoe-test7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvapict/distilbert-base-multilingual-cased-aoe-test7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cvapict/distilbert-base-multilingual-cased-aoe-test7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cvapict/distilbert-base-multilingual-cased-aoe-test7") model = AutoModelForSequenceClassification.from_pretrained("cvapict/distilbert-base-multilingual-cased-aoe-test7") - Notebooks
- Google Colab
- Kaggle
distilbert-base-multilingual-cased-aoe-test7
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1237
- Accuracy: 0.9597
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0918 | 1.0 | 696 | 0.1237 | 0.9597 |
| 0.1161 | 2.0 | 1392 | 0.1127 | 0.9554 |
| 0.08 | 3.0 | 2088 | 0.1172 | 0.9584 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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