metadata
library_name: transformers
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-5-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.057203389830508475
- name: Recall
type: recall
value: 0.040895321440592394
- name: F1
type: f1
value: 0.047693817468105984
- name: Accuracy
type: accuracy
value: 0.7758070168607142
bert-large-5-ner
This model is a fine-tuned version of bert-large-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 1.3380
- Precision: 0.0572
- Recall: 0.0409
- F1: 0.0477
- Accuracy: 0.7758
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 3 | 1.6779 | 0.0398 | 0.0825 | 0.0537 | 0.6719 |
| No log | 2.0 | 6 | 1.4283 | 0.0579 | 0.0527 | 0.0551 | 0.7628 |
| No log | 3.0 | 9 | 1.3380 | 0.0572 | 0.0409 | 0.0477 | 0.7758 |
Framework versions
- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1