---
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: 1.3914
- F1: 0.6372
- Precision: 0.6448
- Recall: 0.6365
- Accuracy: 0.6365

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 1.5405        | 1.0   | 74   | 1.4488          | 0.3206 | 0.2386    | 0.4885 | 0.4885   |
| 1.3156        | 2.0   | 148  | 1.1964          | 0.5541 | 0.5627    | 0.575  | 0.575    |
| 1.0728        | 3.0   | 222  | 1.1077          | 0.6001 | 0.6189    | 0.5981 | 0.5981   |
| 0.9239        | 4.0   | 296  | 1.0742          | 0.6324 | 0.6361    | 0.6365 | 0.6365   |
| 0.7802        | 5.0   | 370  | 1.0834          | 0.6073 | 0.6333    | 0.6058 | 0.6058   |
| 0.661         | 6.0   | 444  | 1.1733          | 0.5984 | 0.6166    | 0.5962 | 0.5962   |
| 0.602         | 7.0   | 518  | 1.1786          | 0.5911 | 0.6193    | 0.5885 | 0.5885   |
| 0.5391        | 8.0   | 592  | 1.2171          | 0.6156 | 0.6251    | 0.6154 | 0.6154   |
| 0.4815        | 9.0   | 666  | 1.2566          | 0.6259 | 0.6399    | 0.625  | 0.625    |
| 0.4548        | 10.0  | 740  | 1.2927          | 0.6233 | 0.6417    | 0.6212 | 0.6212   |
| 0.4538        | 11.0  | 814  | 1.2969          | 0.6385 | 0.6461    | 0.6385 | 0.6385   |
| 0.4119        | 12.0  | 888  | 1.3455          | 0.6376 | 0.6464    | 0.6365 | 0.6365   |
| 0.3968        | 13.0  | 962  | 1.3709          | 0.6304 | 0.6413    | 0.6288 | 0.6288   |
| 0.352         | 14.0  | 1036 | 1.3823          | 0.6246 | 0.6360    | 0.6231 | 0.6231   |
| 0.3551        | 15.0  | 1110 | 1.3914          | 0.6372 | 0.6448    | 0.6365 | 0.6365   |


### Framework versions

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