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

# ops_subcate

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1575
- Accuracy: 0.7428
- F1: 0.7647
- Precision: 0.7715
- Recall: 0.7581

## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 49   | 0.1300          | 0.7291   | 0.7604 | 0.7883    | 0.7345 |
| No log        | 2.0   | 98   | 0.1272          | 0.7391   | 0.7707 | 0.7989    | 0.7444 |
| No log        | 3.0   | 147  | 0.1294          | 0.7391   | 0.7654 | 0.7918    | 0.7407 |
| No log        | 4.0   | 196  | 0.1388          | 0.7341   | 0.7567 | 0.7733    | 0.7407 |
| No log        | 5.0   | 245  | 0.1326          | 0.7541   | 0.7791 | 0.8026    | 0.7568 |
| No log        | 6.0   | 294  | 0.1407          | 0.7478   | 0.7743 | 0.7940    | 0.7556 |
| No log        | 7.0   | 343  | 0.1445          | 0.7341   | 0.7576 | 0.7712    | 0.7444 |
| No log        | 8.0   | 392  | 0.1533          | 0.7528   | 0.7684 | 0.7776    | 0.7593 |
| No log        | 9.0   | 441  | 0.1573          | 0.7628   | 0.7747 | 0.7816    | 0.7680 |
| No log        | 10.0  | 490  | 0.1575          | 0.7428   | 0.7647 | 0.7715    | 0.7581 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1