---
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: 0.1325
- Accuracy: 0.7228
- F1: 0.7556
- Precision: 0.7866
- Recall: 0.7270

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 49   | 0.6030          | 0.0      | 0.0    | 0.0       | 0.0    |
| No log        | 2.0   | 98   | 0.3307          | 0.0      | 0.0    | 0.0       | 0.0    |
| No log        | 3.0   | 147  | 0.3022          | 0.0      | 0.0    | 0.0       | 0.0    |
| No log        | 4.0   | 196  | 0.2354          | 0.3121   | 0.4343 | 0.7171    | 0.3114 |
| No log        | 5.0   | 245  | 0.1831          | 0.5306   | 0.6169 | 0.7313    | 0.5335 |
| No log        | 6.0   | 294  | 0.1580          | 0.6355   | 0.6895 | 0.7504    | 0.6377 |
| No log        | 7.0   | 343  | 0.1408          | 0.6779   | 0.7257 | 0.7702    | 0.6861 |
| No log        | 8.0   | 392  | 0.1325          | 0.7228   | 0.7556 | 0.7866    | 0.7270 |


### Framework versions

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