transformers pytorch tensorboard bert text-classification Analyzation generated_from_trainer base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
license: apache-2.0
metrics:
- accuracy
tags:
- Analyzation
- generated_from_trainer
model-index:
- name: test_trainer
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test_trainer

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4916
- Accuracy: 0.659

## Model description

This model is a fine-tuned model based on Sentiment Analysis or Text Classification for reviews based on the new 'Threads' app.
The reviews dataset can be found on Kaggle.


## Intended uses & limitations

Basically it converts the review text into rating points from 1-5(1 being a very bad review and 5 being a very good review)

## Training and evaluation data

'Reviews' dataset(Thread) from Kaggle.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 250  | 1.0560          | 0.6895   |
| 0.5502        | 2.0   | 500  | 1.3548          | 0.6595   |
| 0.5502        | 3.0   | 750  | 1.4916          | 0.659    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
