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autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735
justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735
autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735 is a text classification model from justpyschitry. 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: 1022634735 - CO2 Emissions (in grams): 16.816741650923202
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jun 23, 2022
- Updated
- Jun 23, 2022
- SHA
- 6c1ff291
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634735
- CO2 Emissions (in grams): 16.816741650923202
Validation Metrics
- Loss: 0.4373569190502167
- Accuracy: 0.9027552674230146
- Macro F1: 0.8938134766263609
- Micro F1: 0.9027552674230146
- Weighted F1: 0.9023653852553881
- Macro Precision: 0.8970541297231431
- Micro Precision: 0.9027552674230146
- Weighted Precision: 0.903514305510645
- Macro Recall: 0.892665778987219
- Micro Recall: 0.9027552674230146
- Weighted Recall: 0.9027552674230146
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/justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735", 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
- Jun 23, 2022
- Updated
- Jun 23, 2022