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--- |
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license: mit |
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language: |
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- en |
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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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tags: |
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- code |
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--- |
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# Model Card for Sentiment Analysis on Primate Dataset |
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This model card provides details about a sentiment analysis model trained on a dataset containing posts related to primates. The model predicts sentiment labels for textual data using transformer-based architectures. |
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## Model Details |
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### Model Description |
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The sentiment analysis model aims to classify text data into sentiment categories such as positive, negative, or neutral. It utilizes transformer-based architectures for sequence classification. |
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- **Developed by:** Jaskaran Singh |
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- **Model type:** Transformer-based sentiment analysis model |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** Transformer-based pre-trained model |
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### Model Sources |
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- **Repository:** https://github.com/JaskaranSingh-01/Sentiment_Analyzer |
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- **Demo:** https://sentimentanalyzer-f76oxwautwypxpea4lj3wg.streamlit.app/ |
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## Uses |
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### Direct Use |
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The model can be directly used for sentiment analysis tasks, particularly on textual data related to primates. |
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### Downstream Use |
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The model can be fine-tuned for specific downstream tasks or integrated into larger applications requiring sentiment analysis functionality. |
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## Bias, Risks, and Limitations |
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### Bias |
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The model's predictions may reflect biases present in the training data, including any biases related to primates or sentiment labeling. |
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### Risks |
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- Misclassification: The model may misclassify sentiment due to ambiguity or complexity in the text. |
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- Generalization: The model's performance may vary across different domains or datasets. |
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### Limitations |
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- Limited Domain: The model's effectiveness may be limited to text related to primates. |
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- Cultural Bias: The model's performance may be influenced by cultural nuances present in the training data. |
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## Recommendations |
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Users should be cautious when interpreting the model's predictions, considering potential biases and limitations. Fine-tuning on domain-specific data or applying post-processing techniques may help mitigate biases and improve performance. |
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## How to Get Started with the Model |
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```python |
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# Example code for using the sentiment analysis model |
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# 1. Load the model and tokenizer |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis_model") |
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model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis_model") |
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# 2. Tokenize input text |
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text = "Sample text for sentiment analysis" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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# 3. Perform inference |
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output = model(**encoded_input) |
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predicted_label = output.logits.argmax().item() |
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# 4. Interpret prediction |
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sentiment_labels = ['Negative', 'Neutral', 'Positive'] |
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print("Predicted Sentiment:", sentiment_labels[predicted_label]) |
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``` |
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## Training Details |
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### Training Data |
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The training data consists of posts related to primates, annotated with sentiment labels. |
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### Training Procedure |
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#### Preprocessing |
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Text data underwent preprocessing steps including lowercase conversion, punctuation removal, tokenization, stopword removal, and stemming. |
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#### Training Hyperparameters |
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- **Training regime:** Fine-tuning of transformer-based pre-trained model |
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- **Optimizer:** Adam optimizer |
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- **Learning rate:** 5e-5 |
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- **Batch size:** 8 |
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- **Epochs:** 10 |
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### Evaluation |
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#### Testing Data, Factors & Metrics |
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- **Testing Data:** Holdout test set |
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- **Metrics:** Accuracy, Precision, Recall, F1-score |
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#### Results |
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- **Accuracy:** 0.79 |
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- **Precision:** 0.74 |
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- **Recall:** 0.77 |
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- **F1-score:** 0.75 |
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## Environmental Impact |
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Carbon emissions were not directly measured for model training. However, users should consider the environmental impact of training and deploying machine learning models, especially on large-scale infrastructure. |
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## Technical Specifications |
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### Model Architecture and Objective |
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The model architecture is based on transformer-based architectures, specifically designed for sequence classification tasks such as sentiment analysis. |
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### Compute Infrastructure |
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#### Software |
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- **Framework:** PyTorch |
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- **Dependencies:** Transformers, NLTK |
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