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

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0499
- Precision: 0.7867
- Recall: 0.8333
- F1: 0.8094
- Accuracy: 0.9842

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 63   | 0.0632          | 0.6793    | 0.7383 | 0.7076 | 0.9789   |
| No log        | 2.0   | 126  | 0.0507          | 0.7559    | 0.8320 | 0.7921 | 0.9837   |
| No log        | 3.0   | 189  | 0.0517          | 0.7771    | 0.8306 | 0.8029 | 0.9840   |
| No log        | 4.0   | 252  | 0.0517          | 0.7822    | 0.8457 | 0.8127 | 0.9839   |
| No log        | 5.0   | 315  | 0.0499          | 0.7867    | 0.8333 | 0.8094 | 0.9842   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3