Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("desonglll/test-ner")
model = AutoModelForTokenClassification.from_pretrained("desonglll/test-ner")Quick Links
test-ner
This model is a fine-tuned version of google-bert/bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0498
- Precision: 0.9466
- Recall: 0.9520
- F1: 0.9493
- Accuracy: 0.9896
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 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for desonglll/test-ner
Base model
google-bert/bert-base-uncasedDataset used to train desonglll/test-ner
Evaluation results
- Precision on conll2003validation set self-reported0.947
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.949
- Accuracy on conll2003validation set self-reported0.990
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="desonglll/test-ner")