metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9948381144840311
- name: Recall
type: recall
value: 0.972891113354671
- name: F1
type: f1
value: 0.9837422213534031
- name: Accuracy
type: accuracy
value: 0.9932984044056051
Bert-NER
This model is a fine-tuned version of bert-base-uncased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0270
- Precision: 0.9948
- Recall: 0.9729
- F1: 0.9837
- Accuracy: 0.9933
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0875 | 1.0 | 501 | 0.0328 | 0.9923 | 0.9696 | 0.9808 | 0.9920 |
| 0.0333 | 2.0 | 1002 | 0.0289 | 0.9935 | 0.9726 | 0.9830 | 0.9929 |
| 0.0283 | 3.0 | 1503 | 0.0270 | 0.9948 | 0.9729 | 0.9837 | 0.9933 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1