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---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9577488309953239
- name: Recall
type: recall
value: 0.9651632446987546
- name: F1
type: f1
value: 0.961441743503772
- name: Accuracy
type: accuracy
value: 0.9907182964622135
---
<!-- 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. -->
# deberta-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Precision: 0.9577
- Recall: 0.9652
- F1: 0.9614
- Accuracy: 0.9907
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0742 | 1.0 | 1756 | 0.0526 | 0.9390 | 0.9510 | 0.9450 | 0.9868 |
| 0.0374 | 2.0 | 3512 | 0.0528 | 0.9421 | 0.9554 | 0.9487 | 0.9879 |
| 0.0205 | 3.0 | 5268 | 0.0505 | 0.9505 | 0.9636 | 0.9570 | 0.9900 |
| 0.0089 | 4.0 | 7024 | 0.0528 | 0.9531 | 0.9636 | 0.9583 | 0.9898 |
| 0.0076 | 5.0 | 8780 | 0.0515 | 0.9577 | 0.9652 | 0.9614 | 0.9907 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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