# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("AnikaAI/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("AnikaAI/bert-finetuned-ner")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: 0.0578
- Precision: 0.9357
- Recall: 0.9507
- F1: 0.9432
- Accuracy: 0.9867
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 |
|---|---|---|---|---|---|---|---|
| 0.0793 | 1.0 | 1756 | 0.0803 | 0.9096 | 0.9345 | 0.9219 | 0.9795 |
| 0.041 | 2.0 | 3512 | 0.0537 | 0.9267 | 0.9465 | 0.9365 | 0.9859 |
| 0.025 | 3.0 | 5268 | 0.0578 | 0.9357 | 0.9507 | 0.9432 | 0.9867 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for AnikaAI/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train AnikaAI/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.936
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.943
- Accuracy on conll2003validation set self-reported0.987
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnikaAI/bert-finetuned-ner")