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

# cv-ner

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0956
- Precision: 0.8906
- Recall: 0.9325
- F1: 0.9111
- Accuracy: 0.9851

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 91   | 0.2049          | 0.6618    | 0.7362 | 0.6970 | 0.9534   |
| 0.5036        | 2.0   | 182  | 0.1156          | 0.7873    | 0.8630 | 0.8234 | 0.9722   |
| 0.1442        | 3.0   | 273  | 0.1078          | 0.8262    | 0.9039 | 0.8633 | 0.9771   |
| 0.0757        | 4.0   | 364  | 0.1179          | 0.8652    | 0.9059 | 0.8851 | 0.9780   |
| 0.0526        | 5.0   | 455  | 0.0907          | 0.888     | 0.9080 | 0.8979 | 0.9837   |
| 0.0342        | 6.0   | 546  | 0.0972          | 0.8926    | 0.9346 | 0.9131 | 0.9832   |
| 0.0245        | 7.0   | 637  | 0.1064          | 0.8937    | 0.9284 | 0.9107 | 0.9834   |
| 0.0188        | 8.0   | 728  | 0.0965          | 0.8980    | 0.9366 | 0.9169 | 0.9850   |
| 0.0159        | 9.0   | 819  | 0.0999          | 0.91      | 0.9305 | 0.9201 | 0.9846   |
| 0.0141        | 10.0  | 910  | 0.0956          | 0.8906    | 0.9325 | 0.9111 | 0.9851   |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1