metadata

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

RoBERTa_Sentiment_Analysis

This model is a fine-tuned version of roberta-base on Twitter Sentiment Analysis 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

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 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