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---
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
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