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README.md

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 license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+tags:
+- code
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+
+# Model Card for Sentiment Analysis on Primate Dataset
+
+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.
+
+## Model Details
+
+### Model Description
+
+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.
+
+- **Developed by:** Jaskaran Singh
+- **Model type:** Transformer-based sentiment analysis model
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Transformer-based pre-trained model
+
+### Model Sources
+
+- **Repository:** https://github.com/JaskaranSingh-01/Sentiment_Analyzer
+- **Demo:** https://sentimentanalyzer-f76oxwautwypxpea4lj3wg.streamlit.app/
+
+## Uses
+
+### Direct Use
+
+The model can be directly used for sentiment analysis tasks, particularly on textual data related to primates.
+
+### Downstream Use
+
+The model can be fine-tuned for specific downstream tasks or integrated into larger applications requiring sentiment analysis functionality.
+
+## Bias, Risks, and Limitations
+
+### Bias
+
+The model's predictions may reflect biases present in the training data, including any biases related to primates or sentiment labeling.
+
+### Risks
+
+- Misclassification: The model may misclassify sentiment due to ambiguity or complexity in the text.
+- Generalization: The model's performance may vary across different domains or datasets.
+
+### Limitations
+
+- Limited Domain: The model's effectiveness may be limited to text related to primates.
+- Cultural Bias: The model's performance may be influenced by cultural nuances present in the training data.
+
+## Recommendations
+
+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.
+
+## How to Get Started with the Model
+
+```python
+# Example code for using the sentiment analysis model
+
+# 1. Load the model and tokenizer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis_model")
+model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis_model")
+
+# 2. Tokenize input text
+text = "Sample text for sentiment analysis"
+encoded_input = tokenizer(text, return_tensors='pt')
+
+# 3. Perform inference
+output = model(**encoded_input)
+predicted_label = output.logits.argmax().item()
+
+# 4. Interpret prediction
+sentiment_labels = ['Negative', 'Neutral', 'Positive']
+print("Predicted Sentiment:", sentiment_labels[predicted_label])
+```
+
+## Training Details
+
+### Training Data
+
+The training data consists of posts related to primates, annotated with sentiment labels.
+
+### Training Procedure
+
+#### Preprocessing
+
+Text data underwent preprocessing steps including lowercase conversion, punctuation removal, tokenization, stopword removal, and stemming.
+
+#### Training Hyperparameters
+
+- **Training regime:** Fine-tuning of transformer-based pre-trained model
+- **Optimizer:** Adam optimizer
+- **Learning rate:** 5e-5
+- **Batch size:** 8
+- **Epochs:** 10
+
+### Evaluation
+
+#### Testing Data, Factors & Metrics
+
+- **Testing Data:** Holdout test set
+- **Metrics:** Accuracy, Precision, Recall, F1-score
+
+#### Results
+
+- **Accuracy:** 0.79
+- **Precision:** 0.74
+- **Recall:** 0.77
+- **F1-score:** 0.75
+
+## Environmental Impact
+
+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.
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model architecture is based on transformer-based architectures, specifically designed for sequence classification tasks such as sentiment analysis.
+
+### Compute Infrastructure
+
+#### Software
+
+- **Framework:** PyTorch
+- **Dependencies:** Transformers, NLTK