Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dibkunnskap/classifier-databases-labels")
model = AutoModelForSequenceClassification.from_pretrained("dibkunnskap/classifier-databases-labels")Quick Links
classifier-databases-labels
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: 0.2850
- F1 Micro: 0.3761
- Roc Auc Micro: 0.8351
- Accuracy (exact Match): 0.1790
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | Roc Auc Micro | Accuracy (exact Match) |
|---|---|---|---|---|---|---|
| 0.3072 | 1.0 | 32380 | 0.2860 | 0.2210 | 0.8192 | 0.1126 |
| 0.2752 | 2.0 | 64760 | 0.2797 | 0.2845 | 0.8311 | 0.1378 |
| 0.2649 | 3.0 | 97140 | 0.2799 | 0.3545 | 0.8355 | 0.1741 |
| 0.2519 | 4.0 | 129520 | 0.2850 | 0.3761 | 0.8351 | 0.1790 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu126
- Datasets 3.4.1
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dibkunnskap/classifier-databases-labels")