---
library_name: transformers
license: apache-2.0
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: RoBERTa_Sentiment_Analysis
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Tweets Hate Speech Detection
      type: tweets-hate-speech-detection/tweets_hate_speech_detection
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9613
    - name: Precision
      type: precision
      value: 0.9626
    - name: Recall
      type: recall
      value: 0.9613
    - name: F1
      type: f1
      value: 0.9619
language:
- en
pipeline_tag: text-classification
datasets:
- tweets-hate-speech-detection/tweets_hate_speech_detection
metrics:
- accuracy
- precision
- recall
- f1
---

<!-- 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. -->

# RoBERTa_Sentiment_Analysis

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on [Twitter Sentiment Analysis](https://www.kaggle.com/datasets/arkhoshghalb/twitter-sentiment-analysis-hatred-speech) dataset

It achieves the following results on the evaluation set:
- Loss: 0.0994
- Accuracy: 0.9613
- Precision: 0.9626
- Recall: 0.9613
- F1_score: 0.9619

## Model description

Fine-tuning performed on a pretrained RoBERTa model. The code can be found [here](https://github.com/atharva-m/Fine-tuning-RoBERTa-for-Sentiment-Analysis)

## Intended uses & limitations

The model is used to classify tweets as either being neutral or hate speech

'test.csv' of Twitter Sentiment Analysis is unused and unlabelled dataset. Contributions in [code](https://github.com/atharva-m/Fine-tuning-RoBERTa-for-Sentiment-Analysis) to utilize the dataset for evaluation are welcome!

## Training and evaluation data

'train.csv' of Twitter Sentiment Analysis is split into training and evaluation sets (80-20)

Fine-tuning was carried out on Google Colab's T4 GPU

## Training procedure

RobertaTokenizerFast is used for tokenizing preprocessed data

Pretrained RobertaForSequenceClassification is used as the classification model

Hyperparameters are defined in TrainingArguments and Trainer is used to train the model

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 50
- eval_batch_size: 50
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- weight_decay : 0.0000001
- report_to="tensorboard"

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1276        | 1.0   | 512  | 0.1116          |
| 0.1097        | 2.0   | 1024 | 0.0994          |
| 0.0662        | 3.0   | 1536 | 0.1165          |
| 0.0542        | 4.0   | 2048 | 0.1447          |
| 0.019         | 5.0   | 2560 | 0.1630          |

### Evaluation results

| Metric    | Value              |
|:---------:|:------------------:|
| Accuracy  | 0.9613639918661036 |
| Precision | 0.9626825763068382 |
| Recall    | 0.9613639918661036 |
| F1-score  | 0.9619595110644236 |

### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1