---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: debert-imeocap
  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. -->

# debert-imeocap

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: 1.8660
- F1: 0.6185
- Precision: 0.6337
- Recall: 0.6154
- Accuracy: 0.6154

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4637        | 1.0   | 74   | 1.3864          | 0.6129 | 0.6262    | 0.6115 | 0.6115   |
| 0.3815        | 2.0   | 148  | 1.3801          | 0.6193 | 0.6348    | 0.6173 | 0.6173   |
| 0.3363        | 3.0   | 222  | 1.6944          | 0.6077 | 0.6297    | 0.6077 | 0.6077   |
| 0.31          | 4.0   | 296  | 1.6945          | 0.5995 | 0.6285    | 0.5942 | 0.5942   |
| 0.2885        | 5.0   | 370  | 1.5945          | 0.6218 | 0.6306    | 0.6192 | 0.6192   |
| 0.2594        | 6.0   | 444  | 1.7662          | 0.6279 | 0.6396    | 0.625  | 0.625    |
| 0.2319        | 7.0   | 518  | 1.7093          | 0.6210 | 0.6321    | 0.6173 | 0.6173   |
| 0.2306        | 8.0   | 592  | 1.8068          | 0.6279 | 0.6341    | 0.6288 | 0.6288   |
| 0.2167        | 9.0   | 666  | 1.7306          | 0.6376 | 0.6444    | 0.6346 | 0.6346   |
| 0.2158        | 10.0  | 740  | 1.8745          | 0.6262 | 0.6318    | 0.6269 | 0.6269   |
| 0.222         | 11.0  | 814  | 1.8323          | 0.6200 | 0.6348    | 0.6173 | 0.6173   |
| 0.2152        | 12.0  | 888  | 1.8576          | 0.6246 | 0.6363    | 0.6212 | 0.6212   |
| 0.226         | 13.0  | 962  | 1.8880          | 0.6343 | 0.6411    | 0.6308 | 0.6308   |
| 0.2097        | 14.0  | 1036 | 1.8884          | 0.6152 | 0.6326    | 0.6115 | 0.6115   |
| 0.2192        | 15.0  | 1110 | 1.8660          | 0.6185 | 0.6337    | 0.6154 | 0.6154   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2