---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-deBERTa-v2-x-large
  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: 0.7384370015948963
    - name: Recall
      type: recall
      value: 0.7377832861189801
    - name: F1
      type: f1
      value: 0.7381099991143388
    - name: Accuracy
      type: accuracy
      value: 0.9460966943038657
---

<!-- 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-v2-x-large

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3963
- Precision: 0.7384
- Recall: 0.7378
- F1: 0.7381
- Accuracy: 0.9461

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 219  | 0.4082          | 0.6932    | 0.7087 | 0.7009 | 0.9386   |
| No log        | 2.0   | 439  | 0.4299          | 0.7467    | 0.6948 | 0.7198 | 0.9426   |
| 0.0094        | 3.0   | 658  | 0.4086          | 0.7435    | 0.7072 | 0.7249 | 0.9441   |
| 0.0094        | 4.0   | 878  | 0.3873          | 0.7426    | 0.7420 | 0.7423 | 0.9461   |
| 0.0054        | 4.99  | 1095 | 0.3963          | 0.7384    | 0.7378 | 0.7381 | 0.9461   |


### Framework versions

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