Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Showroom/accessories_subcategory_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Showroom/accessories_subcategory_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Showroom/accessories_subcategory_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Showroom/accessories_subcategory_classifier") model = AutoModelForSequenceClassification.from_pretrained("Showroom/accessories_subcategory_classifier") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 63188135341
- CO2 Emissions (in grams): 0.7156
Validation Metrics
- Loss: 0.434
- Accuracy: 0.901
- Macro F1: 0.799
- Micro F1: 0.901
- Weighted F1: 0.898
- Macro Precision: 0.887
- Micro Precision: 0.901
- Weighted Precision: 0.906
- Macro Recall: 0.761
- Micro Recall: 0.901
- Weighted Recall: 0.901
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Showroom/autotrain-accessories_categories-63188135341
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Showroom/autotrain-accessories_categories-63188135341", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Showroom/autotrain-accessories_categories-63188135341", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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