Token Classification
Transformers
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Safetensors
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("ShakhzoDavronov/ner-token-classification")
model = AutoModelForTokenClassification.from_pretrained("ShakhzoDavronov/ner-token-classification")Quick Links
ner-token-classification
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0628
- Precision: 0.9369
- Recall: 0.9517
- F1: 0.9442
- Accuracy: 0.9866
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0772 | 1.0 | 1756 | 0.0648 | 0.9013 | 0.9362 | 0.9184 | 0.9823 |
| 0.0345 | 2.0 | 3512 | 0.0656 | 0.9348 | 0.9485 | 0.9416 | 0.9855 |
| 0.0216 | 3.0 | 5268 | 0.0628 | 0.9369 | 0.9517 | 0.9442 | 0.9866 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Model tree for ShakhzoDavronov/ner-token-classification
Base model
google-bert/bert-base-casedDataset used to train ShakhzoDavronov/ner-token-classification
Evaluation results
- Precision on conll2003validation set self-reported0.937
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.987
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ShakhzoDavronov/ner-token-classification")