# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DayCardoso/valueeval24-bert-phase1-initialfreeze")
model = AutoModelForSequenceClassification.from_pretrained("DayCardoso/valueeval24-bert-phase1-initialfreeze")Quick Links
valueeval24-bert-phase1-initialfreeze
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1085
- F1: 0.1171
- Roc Auc: 0.5320
- Accuracy: 0.0580
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: 0.00025
- train_batch_size: 8
- eval_batch_size: 8
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1192 | 1.0 | 2883 | 0.1084 | 0.0885 | 0.5234 | 0.0445 |
| 0.0927 | 2.0 | 5766 | 0.1085 | 0.1171 | 0.5320 | 0.0580 |
Framework versions
- Transformers 4.53.0
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.2
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Model tree for DayCardoso/valueeval24-bert-phase1-initialfreeze
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DayCardoso/valueeval24-bert-phase1-initialfreeze")