---
base_model: microsoft/mdeberta-v3-base
library_name: peft
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base-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. -->

# mdeberta-v3-base-finetuned-ner

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1094
- Precision: 0.9101
- Recall: 0.9567
- F1: 0.9328
- Accuracy: 0.9757

## 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: 0.0002
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2289        | 1.0   | 1167  | 0.1496          | 0.8186    | 0.8964 | 0.8557 | 0.9524   |
| 0.1466        | 2.0   | 2334  | 0.1193          | 0.8771    | 0.9312 | 0.9033 | 0.9677   |
| 0.1064        | 3.0   | 3501  | 0.1143          | 0.8768    | 0.9451 | 0.9097 | 0.9673   |
| 0.0808        | 4.0   | 4668  | 0.0978          | 0.8968    | 0.9491 | 0.9222 | 0.9724   |
| 0.0668        | 5.0   | 5835  | 0.1111          | 0.8889    | 0.9533 | 0.9200 | 0.9713   |
| 0.0559        | 6.0   | 7002  | 0.1168          | 0.9054    | 0.9561 | 0.9301 | 0.9746   |
| 0.0459        | 7.0   | 8169  | 0.1085          | 0.9174    | 0.9457 | 0.9313 | 0.9749   |
| 0.0421        | 8.0   | 9336  | 0.1077          | 0.9141    | 0.9516 | 0.9325 | 0.9759   |
| 0.0324        | 9.0   | 10503 | 0.1084          | 0.9148    | 0.9575 | 0.9357 | 0.9766   |
| 0.03          | 10.0  | 11670 | 0.1094          | 0.9101    | 0.9567 | 0.9328 | 0.9757   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1