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election_relevancy_best
sefaozalpadl/election_relevancy_best
election_relevancy_best is a text classification model from sefaozalpadl. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 23315155 - CO2 Emissions (in grams): 1.3248523193990855
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Repo size
997 MB
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 7, 2021
- SHA
- 6f230777
Try a prompt
Model card
From the Hugging Face model README
Election Fraud Binary Classifier
- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855
Validation Metrics
- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444
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 AutoNLP"}' https://api-inference.huggingface.co/models/sefaozalpadl/election_relevancy_best
Or Python API:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sefaozalpadl/election_relevancy_best")
model = AutoModelForSequenceClassification.from_pretrained("sefaozalpadl/election_relevancy_best")
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Nov 7, 2021