# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("mongdiutindei/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("mongdiutindei/bert-finetuned-ner", device_map="auto")Quick Links
bert-finetuned-ner
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: 0.0590
- Precision: 0.9269
- Recall: 0.9475
- F1: 0.9371
- Accuracy: 0.9860
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 |
|---|---|---|---|---|---|---|---|
| 0.0805 | 1.0 | 1756 | 0.0733 | 0.9035 | 0.9302 | 0.9167 | 0.9795 |
| 0.0409 | 2.0 | 3512 | 0.0583 | 0.9279 | 0.9461 | 0.9369 | 0.9853 |
| 0.0273 | 3.0 | 5268 | 0.0590 | 0.9269 | 0.9475 | 0.9371 | 0.9860 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for mongdiutindei/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train mongdiutindei/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.927
- Recall on conll2003validation set self-reported0.947
- F1 on conll2003validation set self-reported0.937
- Accuracy on conll2003validation set self-reported0.986
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mongdiutindei/bert-finetuned-ner")