# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wennycooper/token-classification-bert-base-uncased")
model = AutoModelForTokenClassification.from_pretrained("wennycooper/token-classification-bert-base-uncased")Quick Links
token-classification-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0480
- Precision: 0.9466
- Recall: 0.9544
- F1: 0.9505
- Accuracy: 0.9899
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.13.3
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Model tree for wennycooper/token-classification-bert-base-uncased
Base model
google-bert/bert-base-uncasedDataset used to train wennycooper/token-classification-bert-base-uncased
Evaluation results
- Precision on conll2003self-reported0.947
- Recall on conll2003self-reported0.954
- F1 on conll2003self-reported0.950
- Accuracy on conll2003self-reported0.990
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wennycooper/token-classification-bert-base-uncased")