---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-base-ner-B
  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-base-ner-B

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on English part of [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0294
- Precision: 0.9660
- Recall: 0.9751
- F1: 0.9705
- Accuracy: 0.9929

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0336        | 0.13  | 257  | 0.0345          | 0.9245    | 0.9386 | 0.9315 | 0.9885   |
| 0.0309        | 0.25  | 514  | 0.0296          | 0.9161    | 0.9624 | 0.9387 | 0.9892   |
| 0.0281        | 0.38  | 771  | 0.0251          | 0.9316    | 0.9539 | 0.9426 | 0.9908   |
| 0.0221        | 0.5   | 1028 | 0.0259          | 0.9381    | 0.9588 | 0.9483 | 0.9910   |
| 0.0234        | 0.63  | 1285 | 0.0260          | 0.9318    | 0.9640 | 0.9477 | 0.9904   |
| 0.0177        | 0.75  | 1542 | 0.0248          | 0.9331    | 0.9665 | 0.9495 | 0.9909   |
| 0.0213        | 0.88  | 1799 | 0.0228          | 0.9522    | 0.9593 | 0.9557 | 0.9918   |
| 0.0252        | 1.0   | 2056 | 0.0233          | 0.9517    | 0.9568 | 0.9542 | 0.9917   |
| 0.0143        | 1.13  | 2313 | 0.0256          | 0.9491    | 0.9641 | 0.9565 | 0.9918   |
| 0.0132        | 1.25  | 2570 | 0.0247          | 0.9536    | 0.9627 | 0.9581 | 0.9921   |
| 0.015         | 1.38  | 2827 | 0.0243          | 0.9467    | 0.9640 | 0.9553 | 0.9917   |
| 0.0148        | 1.5   | 3084 | 0.0254          | 0.9475    | 0.9677 | 0.9575 | 0.9918   |
| 0.0143        | 1.63  | 3341 | 0.0252          | 0.9491    | 0.9667 | 0.9578 | 0.9920   |
| 0.0112        | 1.75  | 3598 | 0.0244          | 0.9546    | 0.9626 | 0.9586 | 0.9923   |
| 0.0074        | 1.88  | 3855 | 0.0268          | 0.9490    | 0.9680 | 0.9584 | 0.9921   |
| 0.0068        | 2.0   | 4112 | 0.0257          | 0.9577    | 0.9610 | 0.9594 | 0.9923   |
| 0.0079        | 2.13  | 4369 | 0.0296          | 0.9457    | 0.9698 | 0.9576 | 0.9919   |
| 0.0067        | 2.26  | 4626 | 0.0290          | 0.9520    | 0.9686 | 0.9602 | 0.9922   |
| 0.0067        | 2.38  | 4883 | 0.0282          | 0.9553    | 0.9653 | 0.9603 | 0.9923   |
| 0.0044        | 2.51  | 5140 | 0.0303          | 0.9600    | 0.9622 | 0.9611 | 0.9926   |
| 0.005         | 2.63  | 5397 | 0.0318          | 0.9488    | 0.9703 | 0.9594 | 0.9920   |
| 0.006         | 2.76  | 5654 | 0.0295          | 0.9564    | 0.9663 | 0.9613 | 0.9925   |
| 0.0059        | 2.88  | 5911 | 0.0304          | 0.9586    | 0.9657 | 0.9621 | 0.9925   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0