---
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: 0.1180
- Precision: 0.9033
- Recall: 0.9347
- F1: 0.9187
- Accuracy: 0.9813

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0663        | 1.0   | 2261  | 0.0715          | 0.8709    | 0.9194 | 0.8945 | 0.9787   |
| 0.0583        | 2.0   | 4522  | 0.0629          | 0.8845    | 0.9267 | 0.9051 | 0.9800   |
| 0.0442        | 3.0   | 6783  | 0.0635          | 0.8841    | 0.9404 | 0.9114 | 0.9802   |
| 0.0402        | 4.0   | 9044  | 0.0588          | 0.9011    | 0.9283 | 0.9145 | 0.9821   |
| 0.0327        | 5.0   | 11305 | 0.0676          | 0.8919    | 0.9385 | 0.9146 | 0.9818   |
| 0.0245        | 6.0   | 13566 | 0.0713          | 0.9037    | 0.9331 | 0.9182 | 0.9821   |
| 0.0183        | 7.0   | 15827 | 0.0848          | 0.9049    | 0.9181 | 0.9114 | 0.9812   |
| 0.0157        | 8.0   | 18088 | 0.0898          | 0.8957    | 0.9411 | 0.9178 | 0.9818   |
| 0.009         | 9.0   | 20349 | 0.1027          | 0.8965    | 0.9385 | 0.9170 | 0.9817   |
| 0.0068        | 10.0  | 22610 | 0.1180          | 0.9033    | 0.9347 | 0.9187 | 0.9813   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3