---
base_model: YituTech/conv-bert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: conv-bert-base
  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. -->

# conv-bert-base

This model is a fine-tuned version of [YituTech/conv-bert-base](https://huggingface.co/YituTech/conv-bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2024
- Precision: 0.7686
- Recall: 0.8278
- F1: 0.7971
- Accuracy: 0.9376

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2235        | 1.0   | 2078 | 0.2225          | 0.7307    | 0.7996 | 0.7636 | 0.9301   |
| 0.1814        | 2.0   | 4156 | 0.1946          | 0.7539    | 0.8257 | 0.7881 | 0.9363   |
| 0.1469        | 3.0   | 6234 | 0.2024          | 0.7686    | 0.8278 | 0.7971 | 0.9376   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1