Text Classification
Transformers
Safetensors
sentence-transformers
English
bert
transaction-classification
banking
finance
MiniLM
data-augmentation
experimental
Eval Results (legacy)
text-embeddings-inference
Instructions to use maaz-zaidi/transaction-classifier-minilm-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maaz-zaidi/transaction-classifier-minilm-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maaz-zaidi/transaction-classifier-minilm-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maaz-zaidi/transaction-classifier-minilm-augmented") model = AutoModelForSequenceClassification.from_pretrained("maaz-zaidi/transaction-classifier-minilm-augmented") - sentence-transformers
How to use maaz-zaidi/transaction-classifier-minilm-augmented with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("maaz-zaidi/transaction-classifier-minilm-augmented") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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