# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("doomnova/distilbert_system_B")
model = AutoModelForTokenClassification.from_pretrained("doomnova/distilbert_system_B")Quick Links
distilbert_system_B
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0293
- Precision: 0.9456
- Recall: 0.9531
- F1: 0.9493
- Accuracy: 0.9931
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0134 | 1.0 | 8205 | 0.0208 | 0.9466 | 0.9488 | 0.9477 | 0.9929 |
| 0.0067 | 2.0 | 16410 | 0.0237 | 0.9453 | 0.9528 | 0.9490 | 0.9931 |
| 0.0031 | 3.0 | 24615 | 0.0293 | 0.9456 | 0.9531 | 0.9493 | 0.9931 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for doomnova/distilbert_system_B
Base model
distilbert/distilbert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="doomnova/distilbert_system_B")