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---
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
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