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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: results
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: 0.9307273626917367
- name: Recall
type: recall
value: 0.9496802423426456
- name: F1
type: f1
value: 0.9401082882132445
- name: Accuracy
type: accuracy
value: 0.9863866486136458
---
<!-- 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. -->
# results
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0635
- Precision: 0.9307
- Recall: 0.9497
- F1: 0.9401
- Accuracy: 0.9864
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2313 | 0.2847 | 500 | 0.1403 | 0.8444 | 0.8696 | 0.8568 | 0.9626 |
| 0.1088 | 0.5695 | 1000 | 0.0887 | 0.8717 | 0.9098 | 0.8903 | 0.9765 |
| 0.1211 | 0.8542 | 1500 | 0.0846 | 0.9076 | 0.9238 | 0.9156 | 0.9784 |
| 0.0503 | 1.1390 | 2000 | 0.0753 | 0.9101 | 0.9354 | 0.9226 | 0.9814 |
| 0.0493 | 1.4237 | 2500 | 0.0630 | 0.9170 | 0.9421 | 0.9294 | 0.9833 |
| 0.0624 | 1.7084 | 3000 | 0.0705 | 0.9277 | 0.9366 | 0.9321 | 0.9837 |
| 0.0313 | 1.9932 | 3500 | 0.0675 | 0.9270 | 0.9426 | 0.9347 | 0.9843 |
| 0.0335 | 2.2779 | 4000 | 0.0661 | 0.9284 | 0.9492 | 0.9387 | 0.9857 |
| 0.0098 | 2.5626 | 4500 | 0.0693 | 0.9347 | 0.9473 | 0.9410 | 0.9849 |
| 0.0099 | 2.8474 | 5000 | 0.0635 | 0.9307 | 0.9497 | 0.9401 | 0.9864 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1
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