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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- sentiment-analysis |
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- nlp |
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- distilbert |
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base_model: distilbert-base-uncased |
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--- |
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# 🤖 My Fine-Tuned Sentiment Analysis Model |
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This model is a fine-tuned version of **DistilBERT** designed for sentiment analysis. It analyzes text and predicts whether the sentiment is **POSITIVE** or **NEGATIVE** (or specific labels depending on your training). |
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## 📊 Model Details |
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- **Model Architecture:** DistilBERT |
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- **Task:** Text Classification (Sentiment Analysis) |
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- **Language:** English |
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- **License:** MIT |
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## 🚀 How to Use |
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You can use this model directly with the Hugging Face `pipeline` in just a few lines of code: |
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```python |
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from transformers import pipeline |
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# 1. Load the pipeline |
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classifier = pipeline("text-classification", model="Rcids/my-finetuned-model") |
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# 2. Test it out |
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text = "I absolutely loved this product! It was amazing." |
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result = classifier(text) |
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print(result) |
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# Output: [{'label': 'POSITIVE', 'score': 0.99}] |
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## 🔧 Training Details |
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This model was fine-tuned on a custom dataset to improve performance on specific sentiment tasks compared to the base generic model. |
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- **Optimizer:** AdamW |
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- **Framework:** PyTorch |
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- **Base Model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) |
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## ⚠️ Limitations |
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- The model performance depends on the domain of the data it was trained on. |
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- It may not detect sarcasm or subtle nuances in complex sentences. |
