# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("DerivedFunction01/distilbert-finance-sec")
model = AutoModelForMaskedLM.from_pretrained("DerivedFunction01/distilbert-finance-sec")Quick Links
distilbert-dapt
This model is a fine-tuned version of distilbert/distilbert-base-uncased on a dataset. It achieves the following results on the evaluation set:
- Loss: 1.3623
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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8489 | 0.1195 | 500 | 1.7333 |
| 1.6703 | 0.2390 | 1000 | 1.5837 |
| 1.5914 | 0.3585 | 1500 | 1.5023 |
| 1.5805 | 0.4780 | 2000 | 1.4578 |
| 1.5379 | 0.5975 | 2500 | 1.4236 |
| 1.4827 | 0.7170 | 3000 | 1.4011 |
| 1.4549 | 0.8365 | 3500 | 1.3739 |
| 1.4450 | 0.9560 | 4000 | 1.3623 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.2
- Downloads last month
- 9
Model tree for DerivedFunction01/distilbert-finance-sec
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DerivedFunction01/distilbert-finance-sec")