README.md

---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-large-finetuned-ner
  results: []
---

<!-- 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-v3-large-finetuned-ner

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0364
- Precision: 0.9641
- Recall: 0.9716
- F1: 0.9678
- Accuracy: 0.9931

## 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: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1237        | 1.0   | 878  | 0.0406          | 0.9492    | 0.9589 | 0.9540 | 0.9906   |
| 0.0242        | 2.0   | 1756 | 0.0340          | 0.9550    | 0.9634 | 0.9592 | 0.9917   |
| 0.0123        | 3.0   | 2634 | 0.0383          | 0.9630    | 0.9679 | 0.9654 | 0.9923   |
| 0.0055        | 4.0   | 3512 | 0.0345          | 0.9633    | 0.9716 | 0.9674 | 0.9929   |
| 0.0034        | 5.0   | 4390 | 0.0364          | 0.9641    | 0.9716 | 0.9678 | 0.9931   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2