# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("thepian/checkpoints")
model = AutoModelForTokenClassification.from_pretrained("thepian/checkpoints")Quick Links
checkpoints
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2159
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1877 | 1.0 | 1251 | 0.1840 |
| 0.1032 | 2.0 | 2502 | 0.1696 |
| 0.0553 | 3.0 | 3753 | 0.1936 |
| 0.0292 | 4.0 | 5004 | 0.2040 |
| 0.0256 | 5.0 | 6255 | 0.2159 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0
- Datasets 3.2.0
- Tokenizers 0.22.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="thepian/checkpoints")