---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: reco-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. -->

# reco-ner

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0668
- Precision: 0.8125
- Recall: 0.8790
- F1: 0.8444
- Accuracy: 0.9819

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4516        | 1.0   | 626  | 0.4047          | 0.4332    | 0.4564 | 0.4445 | 0.8980   |
| 0.3677        | 2.0   | 1252 | 0.2774          | 0.4918    | 0.5731 | 0.5293 | 0.9193   |
| 0.2892        | 3.0   | 1878 | 0.2133          | 0.6139    | 0.6581 | 0.6353 | 0.9384   |
| 0.2736        | 4.0   | 2504 | 0.1772          | 0.6248    | 0.6854 | 0.6537 | 0.9488   |
| 0.221         | 5.0   | 3130 | 0.1503          | 0.6295    | 0.7328 | 0.6772 | 0.9560   |
| 0.1569        | 6.0   | 3756 | 0.1283          | 0.6821    | 0.8108 | 0.7409 | 0.9623   |
| 0.1534        | 7.0   | 4382 | 0.0995          | 0.7412    | 0.8119 | 0.7749 | 0.9708   |
| 0.089         | 8.0   | 5008 | 0.0846          | 0.7695    | 0.8353 | 0.8010 | 0.9760   |
| 0.0923        | 9.0   | 5634 | 0.0743          | 0.7881    | 0.8740 | 0.8289 | 0.9789   |
| 0.0711        | 10.0  | 6260 | 0.0668          | 0.8125    | 0.8790 | 0.8444 | 0.9819   |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1