# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("aslon1213/bert-finetuned-ner1")
model = AutoModelForTokenClassification.from_pretrained("aslon1213/bert-finetuned-ner1")Quick Links
bert-finetuned-ner1
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0493
- Precision: 0.5627
- Recall: 0.3880
- F1: 0.4593
- Accuracy: 0.9888
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
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0677 | 0.0444 | 5000 | 0.0493 | 0.5627 | 0.3880 | 0.4593 | 0.9888 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 5
Model tree for aslon1213/bert-finetuned-ner1
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aslon1213/bert-finetuned-ner1")