metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: modernbert-base-conll2003-english-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.7553173672751633
- name: Recall
type: recall
value: 0.7985127478753541
- name: F1
type: f1
value: 0.776314657027283
- name: Accuracy
type: accuracy
value: 0.9627651555938409
modernbert-base-conll2003-english-ner
This model is a fine-tuned version of answerdotai/ModernBERT-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1457
- Precision: 0.7553
- Recall: 0.7985
- F1: 0.7763
- Accuracy: 0.9628
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 439 | 0.1737 | 0.6772 | 0.7236 | 0.6996 | 0.9521 |
| 0.2272 | 2.0 | 878 | 0.1518 | 0.7403 | 0.7840 | 0.7615 | 0.9605 |
| 0.1047 | 3.0 | 1317 | 0.1459 | 0.7522 | 0.7937 | 0.7724 | 0.9625 |
| 0.0835 | 4.0 | 1756 | 0.1460 | 0.7514 | 0.7964 | 0.7733 | 0.9626 |
| 0.076 | 5.0 | 2195 | 0.1457 | 0.7553 | 0.7985 | 0.7763 | 0.9628 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0