---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-deBERTa-v3-large-conll2003
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9235068110373734
    - name: Recall
      type: recall
      value: 0.9362606232294618
    - name: F1
      type: f1
      value: 0.9298399859328293
    - name: Accuracy
      type: accuracy
      value: 0.9853128028426833
---

<!-- 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. -->

# ner-deBERTa-v3-large-conll2003

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1546
- Precision: 0.9235
- Recall: 0.9363
- F1: 0.9298
- Accuracy: 0.9853

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0077        | 1.0   | 878  | 0.1280          | 0.9096    | 0.9265 | 0.9180 | 0.9832   |
| 0.0084        | 2.0   | 1756 | 0.1380          | 0.9167    | 0.9299 | 0.9233 | 0.9844   |
| 0.0037        | 3.0   | 2634 | 0.1495          | 0.9221    | 0.9347 | 0.9283 | 0.9850   |
| 0.0015        | 4.0   | 3512 | 0.1517          | 0.9215    | 0.9347 | 0.9280 | 0.9849   |
| 0.0006        | 5.0   | 4390 | 0.1546          | 0.9235    | 0.9363 | 0.9298 | 0.9853   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3