| --- |
| license: mit |
| library_name: transformers |
| datasets: |
| - stanfordnlp/sst2 |
| language: |
| - en |
| base_model: |
| - distilbert/distilbert-base-uncased |
| pipeline_tag: text-classification |
| tags: |
| - legal |
| - PyTorch |
| - text-classification |
| - sentiment-analysis |
| --- |
| |
| # Simple Text Classifier |
|
|
| This is a fine-tuned model for text classification based on `distilbert-base-uncased`. |
|
|
| ## Model Details |
|
|
| - Model Type: Text Classification |
| - Number of Classes: 2 |
| - Hidden Size: 768 |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoTokenizer |
| from huggingface_text_classifier.model import SimpleTextClassifier |
| |
| # Load model and tokenizer |
| tokenizer = AutoTokenizer.from_pretrained("ajinathgh/sentiment_analysis") |
| model = SimpleTextClassifier.from_pretrained("ajinathgh/sentiment_analysis") |
| |
| # Prepare input |
| inputs = tokenizer("Example text to classify", return_tensors="pt") |
| |
| # Get predictions |
| outputs = model(**inputs) |
| predicted_class = outputs.argmax(-1).item() |
| ``` |
|
|
