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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: 0.9267369114257491
- name: Recall
type: recall
value: 0.9473241332884551
- name: F1
type: f1
value: 0.9369174434087884
- name: Accuracy
type: accuracy
value: 0.9852239948195679
---
<!-- 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: 0.1060
- Precision: 0.9267
- Recall: 0.9473
- F1: 0.9369
- Accuracy: 0.9852
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1012 | 1.0 | 1756 | 0.0895 | 0.8924 | 0.9194 | 0.9057 | 0.9767 |
| 0.0491 | 2.0 | 3512 | 0.0818 | 0.9070 | 0.9260 | 0.9164 | 0.9801 |
| 0.0334 | 3.0 | 5268 | 0.0818 | 0.9170 | 0.9315 | 0.9242 | 0.9821 |
| 0.0235 | 4.0 | 7024 | 0.0893 | 0.9074 | 0.9364 | 0.9216 | 0.9815 |
| 0.0167 | 5.0 | 8780 | 0.0879 | 0.9106 | 0.9414 | 0.9258 | 0.9828 |
| 0.0071 | 6.0 | 10536 | 0.0955 | 0.9172 | 0.9435 | 0.9301 | 0.9836 |
| 0.0039 | 7.0 | 12292 | 0.1016 | 0.9209 | 0.9423 | 0.9315 | 0.9835 |
| 0.0021 | 8.0 | 14048 | 0.1043 | 0.9294 | 0.9463 | 0.9378 | 0.9847 |
| 0.0014 | 9.0 | 15804 | 0.1064 | 0.9271 | 0.9475 | 0.9372 | 0.9853 |
| 0.0005 | 10.0 | 17560 | 0.1060 | 0.9267 | 0.9473 | 0.9369 | 0.9852 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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