Text Classification
Transformers
PyTorch
English
distilbert
fill-mask
legal
PyTorch
sentiment-analysis
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ajinathgh/sentiment_analysis")
model = AutoModelForMaskedLM.from_pretrained("ajinathgh/sentiment_analysis")Quick Links
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
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()
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Model tree for ajinathgh/sentiment_analysis
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ajinathgh/sentiment_analysis")