|
--- |
|
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-ner-conll2003 |
|
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: 0.8349195930423368 |
|
- name: Recall |
|
type: recall |
|
value: 0.856277347694379 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8454636091724825 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9751567306569059 |
|
language: |
|
- en |
|
pipeline_tag: token-classification |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# modernbert-ner-conll2003 |
|
|
|
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the conll2003 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0992 |
|
- Precision: 0.8349 |
|
- Recall: 0.8563 |
|
- F1: 0.8455 |
|
- Accuracy: 0.9752 |
|
|
|
## 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-06 |
|
- 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2306 | 1.0 | 1756 | 0.2243 | 0.6074 | 0.6483 | 0.6272 | 0.9406 | |
|
| 0.1415 | 2.0 | 3512 | 0.1583 | 0.7258 | 0.7536 | 0.7394 | 0.9583 | |
|
| 0.1143 | 3.0 | 5268 | 0.1335 | 0.7731 | 0.7989 | 0.7858 | 0.9657 | |
|
| 0.0913 | 4.0 | 7024 | 0.1145 | 0.7958 | 0.8256 | 0.8104 | 0.9699 | |
|
| 0.0848 | 5.0 | 8780 | 0.1079 | 0.8120 | 0.8408 | 0.8261 | 0.9720 | |
|
| 0.0728 | 6.0 | 10536 | 0.1036 | 0.8214 | 0.8452 | 0.8331 | 0.9730 | |
|
| 0.0623 | 7.0 | 12292 | 0.1032 | 0.8258 | 0.8487 | 0.8371 | 0.9737 | |
|
| 0.0599 | 8.0 | 14048 | 0.0990 | 0.8289 | 0.8527 | 0.8406 | 0.9745 | |
|
| 0.0558 | 9.0 | 15804 | 0.0998 | 0.8331 | 0.8541 | 0.8434 | 0.9750 | |
|
| 0.0559 | 10.0 | 17560 | 0.0992 | 0.8349 | 0.8563 | 0.8455 | 0.9752 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.0.dev0 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
