# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("ZON8955/NER0709")
model = AutoModelForTokenClassification.from_pretrained("ZON8955/NER0709")Quick Links
NER0709
This model is a fine-tuned version of bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
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: 8
- 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 9 | 0.0090 |
| 0.4456 | 2.0 | 18 | 0.0006 |
| 0.0107 | 3.0 | 27 | 0.0003 |
| 0.0009 | 4.0 | 36 | 0.0002 |
| 0.0005 | 5.0 | 45 | 0.0002 |
| 0.0005 | 6.0 | 54 | 0.0002 |
Framework versions
- Transformers 4.53.0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
- Downloads last month
- 4
Model tree for ZON8955/NER0709
Base model
google-bert/bert-base-chinese
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ZON8955/NER0709")