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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: RoBERTa_Sentiment_Analysis |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Tweets Hate Speech Detection |
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type: tweets-hate-speech-detection/tweets_hate_speech_detection |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9613 |
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- name: Precision |
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type: precision |
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value: 0.9626 |
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- name: Recall |
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type: recall |
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value: 0.9613 |
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- name: F1 |
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type: f1 |
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value: 0.9619 |
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language: |
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- en |
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pipeline_tag: text-classification |
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datasets: |
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- tweets-hate-speech-detection/tweets_hate_speech_detection |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# RoBERTa_Sentiment_Analysis |
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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 |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0994 |
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- Accuracy: 0.9613 |
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- Precision: 0.9626 |
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- Recall: 0.9613 |
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- F1_score: 0.9619 |
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## Model description |
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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) |
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## Intended uses & limitations |
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The model is used to classify tweets as either being neutral or hate speech |
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'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! |
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## Training and evaluation data |
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'train.csv' of Twitter Sentiment Analysis is split into training and evaluation sets (80-20) |
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Fine-tuning was carried out on Google Colab's T4 GPU |
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## Training procedure |
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RobertaTokenizerFast is used for tokenizing preprocessed data |
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Pretrained RobertaForSequenceClassification is used as the classification model |
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Hyperparameters are defined in TrainingArguments and Trainer is used to train the model |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 50 |
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- eval_batch_size: 50 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- weight_decay : 0.0000001 |
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- report_to="tensorboard" |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.1276 | 1.0 | 512 | 0.1116 | |
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| 0.1097 | 2.0 | 1024 | 0.0994 | |
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| 0.0662 | 3.0 | 1536 | 0.1165 | |
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| 0.0542 | 4.0 | 2048 | 0.1447 | |
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| 0.019 | 5.0 | 2560 | 0.1630 | |
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### Evaluation results |
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| Metric | Value | |
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|:---------:|:------------------:| |
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| Accuracy | 0.9613639918661036 | |
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| Precision | 0.9626825763068382 | |
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| Recall | 0.9613639918661036 | |
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| F1-score | 0.9619595110644236 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
