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--- |
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language: "en" |
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license: "mit" |
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tags: |
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- distilbert |
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- sentiment-analysis |
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- multilingual |
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widgets: |
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- text: "I love this movie!" |
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--- |
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# Model Name: DistilBERT for Sentiment Analysis |
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## Model Description |
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### Overview |
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This model is a fine-tuned version of `distilbert-base-uncased` on a social media dataset for the purpose of sentiment analysis. It can classify text into non-negative and negative sentiments. |
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### Intended Use |
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This model is intended for sentiment analysis tasks, particularly for analyzing social media texts. |
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### Model Architecture |
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This model is based on the `DistilBertForSequenceClassification` architecture, a distilled version of BERT that maintains comparable performance on downstream tasks while being more computationally efficient. |
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## Training |
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### Training Data |
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The model was trained on a dataset consisting of social media posts, surveys and interviews, labeled for sentiment (non-negative and negative). The dataset includes texts from a variety of sources and demographics. |
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### Training Procedure |
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The model was trained using the following parameters: |
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- Optimizer: AdamW |
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- Learning Rate: 5e-5 |
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- Batch Size: 32 |
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- Epochs: 30 |
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Training was conducted on Kaggle, utilizing two GPUs for accelerated training. |
