Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Rugs25/simple-sentiment-analyzer")
model = AutoModelForSequenceClassification.from_pretrained("Rugs25/simple-sentiment-analyzer", device_map="auto")Quick Links
Simple Sentiment Analyzer
🚀 This is a sentiment analysis model trained to classify text as Positive or Negative.
Model Details
- Architecture: DistilBERT (fine-tuned for sentiment classification)
- Framework: 🤗 Transformers
- Files included:
config.jsonmodel.safetensorstokenizer.jsontokenizer_config.jsonspecial_tokens_map.jsonvocab.txt
Usage
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="Rugs25/simple-sentiment-analyzer")
print(classifier("I love Hugging Face!")) # → POSITIVE
print(classifier("This is terrible.")) # → NEGATIVE
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rugs25/simple-sentiment-analyzer")