---
license: mit
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-finetuned-ner-connll-late-stop
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: en
      split: train
      args: en
    metrics:
    - name: Precision
      type: precision
      value: 0.830192600803658
    - name: Recall
      type: recall
      value: 0.8470945850417079
    - name: F1
      type: f1
      value: 0.8385584324702589
    - name: Accuracy
      type: accuracy
      value: 0.9228861596598961
---

<!-- 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-connll-late-stop

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5259
- Precision: 0.8302
- Recall: 0.8471
- F1: 0.8386
- Accuracy: 0.9229

## 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: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3408        | 1.0   | 1875  | 0.3639          | 0.7462    | 0.7887 | 0.7669 | 0.8966   |
| 0.2435        | 2.0   | 3750  | 0.2933          | 0.8104    | 0.8332 | 0.8217 | 0.9178   |
| 0.1822        | 3.0   | 5625  | 0.3034          | 0.8147    | 0.8388 | 0.8266 | 0.9221   |
| 0.1402        | 4.0   | 7500  | 0.3667          | 0.8275    | 0.8474 | 0.8374 | 0.9235   |
| 0.1013        | 5.0   | 9375  | 0.4290          | 0.8285    | 0.8448 | 0.8366 | 0.9227   |
| 0.0677        | 6.0   | 11250 | 0.4914          | 0.8259    | 0.8473 | 0.8365 | 0.9231   |
| 0.0439        | 7.0   | 13125 | 0.5259          | 0.8302    | 0.8471 | 0.8386 | 0.9229   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1