README.md

---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: SentimentT2_BertBase
  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. -->

# SentimentT2_BertBase

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3196
- Accuracy: 0.8706
- F1: 0.8670
- Auc Roc: 0.9473
- Log Loss: 0.3196

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Auc Roc | Log Loss |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:|
| 0.7054        | 1.0   | 101  | 0.6628          | 0.6045   | 0.5047 | 0.7215  | 0.6628   |
| 0.6303        | 2.0   | 203  | 0.5437          | 0.7823   | 0.7842 | 0.8748  | 0.5437   |
| 0.4599        | 3.0   | 304  | 0.3532          | 0.8520   | 0.8449 | 0.9364  | 0.3532   |
| 0.3413        | 4.0   | 406  | 0.3172          | 0.8719   | 0.8733 | 0.9405  | 0.3172   |
| 0.2877        | 4.98  | 505  | 0.3196          | 0.8706   | 0.8670 | 0.9473  | 0.3196   |


### Framework versions

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