---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-large-finetuned-ner-10epochs-V2
  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-10epochs-V2

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0941
- Precision: 0.9139
- Recall: 0.9358
- F1: 0.9248
- Accuracy: 0.9856

## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0539        | 1.0   | 2261  | 0.0571          | 0.8751    | 0.9304 | 0.9019 | 0.9822   |
| 0.0481        | 2.0   | 4522  | 0.0515          | 0.8794    | 0.9387 | 0.9081 | 0.9833   |
| 0.0362        | 3.0   | 6783  | 0.0502          | 0.8956    | 0.9336 | 0.9142 | 0.9841   |
| 0.0341        | 4.0   | 9044  | 0.0456          | 0.9097    | 0.9301 | 0.9198 | 0.9856   |
| 0.0272        | 5.0   | 11305 | 0.0520          | 0.9005    | 0.9451 | 0.9223 | 0.9860   |
| 0.0214        | 6.0   | 13566 | 0.0583          | 0.9069    | 0.9330 | 0.9197 | 0.9855   |
| 0.0162        | 7.0   | 15827 | 0.0684          | 0.9154    | 0.9259 | 0.9206 | 0.9854   |
| 0.0129        | 8.0   | 18088 | 0.0736          | 0.9158    | 0.9339 | 0.9248 | 0.9854   |
| 0.0074        | 9.0   | 20349 | 0.0869          | 0.9091    | 0.9355 | 0.9221 | 0.9854   |
| 0.0049        | 10.0  | 22610 | 0.0941          | 0.9139    | 0.9358 | 0.9248 | 0.9856   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3