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accessories_subcategory_classifier
Showroom/accessories_subcategory_classifier
accessories_subcategory_classifier is a text classification model from Showroom. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 63188135341 - CO2 Emissions (in grams): 0.7156
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Model card
From the Hugging Face model README
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)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- Languages
- en
- Created
- May 31, 2023
- Updated
- May 31, 2023