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-200-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: 0.7622442653440794
- name: Recall
type: recall
value: 0.8276674520363514
- name: F1
type: f1
value: 0.7936098111989672
- name: Accuracy
type: accuracy
value: 0.968322884622873
bert-large-200-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: 0.1527
- Precision: 0.7622
- Recall: 0.8277
- F1: 0.7936
- Accuracy: 0.9683
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 | 100 | 0.3317 | 0.4836 | 0.5545 | 0.5167 | 0.9237 |
| No log | 2.0 | 200 | 0.1819 | 0.7122 | 0.7763 | 0.7429 | 0.9607 |
| No log | 3.0 | 300 | 0.1527 | 0.7622 | 0.8277 | 0.7936 | 0.9683 |
Framework versions
- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1