---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results-yelp
  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. -->

# results-yelp

This model is a fine-tuned version of [textattack/bert-base-uncased-yelp-polarity](https://huggingface.co/textattack/bert-base-uncased-yelp-polarity) on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

`tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))`

## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment.

## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.

## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation.

<!-- ## Training procedure -->

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- 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: 3

### Training results

The training loss obtained was 0.265741667.

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
