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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-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: 0.9306692773228907
- name: Recall
type: recall
value: 0.9381841019199713
- name: F1
type: f1
value: 0.9344115807345187
- name: Accuracy
type: accuracy
value: 0.9832666156472597
---
<!-- 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: 0.1183
- Precision: 0.9307
- Recall: 0.9382
- F1: 0.9344
- Accuracy: 0.9833
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1081 | 1.0 | 1756 | 0.0963 | 0.8947 | 0.8982 | 0.8964 | 0.9742 |
| 0.0518 | 2.0 | 3512 | 0.0780 | 0.9219 | 0.9182 | 0.9200 | 0.9803 |
| 0.0348 | 3.0 | 5268 | 0.0833 | 0.9258 | 0.9271 | 0.9264 | 0.9819 |
| 0.0268 | 4.0 | 7024 | 0.0900 | 0.9152 | 0.9241 | 0.9196 | 0.9805 |
| 0.0167 | 5.0 | 8780 | 0.0929 | 0.9225 | 0.9320 | 0.9272 | 0.9822 |
| 0.0071 | 6.0 | 10536 | 0.1119 | 0.9229 | 0.9270 | 0.9249 | 0.9816 |
| 0.0056 | 7.0 | 12292 | 0.1073 | 0.9286 | 0.9366 | 0.9326 | 0.9832 |
| 0.0021 | 8.0 | 14048 | 0.1194 | 0.9285 | 0.9350 | 0.9318 | 0.9829 |
| 0.0019 | 9.0 | 15804 | 0.1156 | 0.9318 | 0.9376 | 0.9347 | 0.9833 |
| 0.0011 | 10.0 | 17560 | 0.1183 | 0.9307 | 0.9382 | 0.9344 | 0.9833 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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