Downloads · 30 days
13
28% of all-time downloads
product_type_classifier
Showroom/product_type_classifier
product_type_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: 60925134561 - CO2 Emissions (in grams): 0.8692
All-time downloads
46
Public
Repo size
2.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.3 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- May 23, 2023
- Updated
- May 23, 2023
- SHA
- 90838844
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 60925134561
- CO2 Emissions (in grams): 0.8692
Validation Metrics
- Loss: 0.109
- Accuracy: 0.972
- Macro F1: 0.973
- Micro F1: 0.972
- Weighted F1: 0.972
- Macro Precision: 0.973
- Micro Precision: 0.972
- Weighted Precision: 0.972
- Macro Recall: 0.973
- Micro Recall: 0.972
- Weighted Recall: 0.972
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-product_categories-60925134561
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Showroom/autotrain-product_categories-60925134561", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Showroom/autotrain-product_categories-60925134561", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- May 23, 2023
- Updated
- May 23, 2023