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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: 0.9254922831293241
- name: Recall
type: recall
value: 0.9361205813744842
- name: F1
type: f1
value: 0.9307760927743086
- name: Accuracy
type: accuracy
value: 0.9831488785541885
---
<!-- 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: 0.0966
- Precision: 0.9255
- Recall: 0.9361
- F1: 0.9308
- Accuracy: 0.9831
## 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: 2147483647
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1045 | 1.0 | 1756 | 0.0891 | 0.8908 | 0.9032 | 0.8970 | 0.9747 |
| 0.044 | 2.0 | 3512 | 0.0809 | 0.9209 | 0.9175 | 0.9192 | 0.9793 |
| 0.0253 | 3.0 | 5268 | 0.0806 | 0.9268 | 0.9280 | 0.9274 | 0.9821 |
| 0.0129 | 4.0 | 7024 | 0.0909 | 0.9301 | 0.9341 | 0.9321 | 0.9829 |
| 0.0042 | 5.0 | 8780 | 0.0966 | 0.9255 | 0.9361 | 0.9308 | 0.9831 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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