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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-cased
model-index:
- name: distilbert-base-cased-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- type: precision
value: 0.932077342588002
name: Precision
- type: recall
value: 0.9491753618310333
name: Recall
- type: f1
value: 0.940548653381139
name: F1
- type: accuracy
value: 0.984782480720551
name: Accuracy
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- type: accuracy
value: 0.8975276153858275
name: Accuracy
verified: true
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- type: precision
value: 0.9258126323573902
name: Precision
verified: true
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- type: recall
value: 0.9132871306827602
name: Recall
verified: true
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- type: auc
value: NaN
name: AUC
verified: true
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- type: f1
value: 0.9195072279905185
name: F1
verified: true
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- type: loss
value: 0.8574212193489075
name: loss
verified: true
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---
<!-- 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. -->
# distilbert-base-cased-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1088
- Precision: 0.9321
- Recall: 0.9492
- F1: 0.9405
- Accuracy: 0.9848
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2147483647
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1015 | 1.0 | 1756 | 0.1001 | 0.8858 | 0.9167 | 0.9010 | 0.9740 |
| 0.049 | 2.0 | 3512 | 0.0803 | 0.8993 | 0.9273 | 0.9131 | 0.9798 |
| 0.0327 | 3.0 | 5268 | 0.0794 | 0.9199 | 0.9350 | 0.9274 | 0.9821 |
| 0.0237 | 4.0 | 7024 | 0.0880 | 0.9050 | 0.9344 | 0.9194 | 0.9813 |
| 0.0131 | 5.0 | 8780 | 0.0849 | 0.9178 | 0.9446 | 0.9310 | 0.9837 |
| 0.0073 | 6.0 | 10536 | 0.0975 | 0.9166 | 0.9446 | 0.9304 | 0.9838 |
| 0.0044 | 7.0 | 12292 | 0.0965 | 0.9267 | 0.9475 | 0.9370 | 0.9842 |
| 0.0015 | 8.0 | 14048 | 0.1075 | 0.9273 | 0.9463 | 0.9367 | 0.9843 |
| 0.0011 | 9.0 | 15804 | 0.1089 | 0.9317 | 0.9480 | 0.9398 | 0.9847 |
| 0.0006 | 10.0 | 17560 | 0.1088 | 0.9321 | 0.9492 | 0.9405 | 0.9848 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|