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stop_the_steal_relevancy_analysis-binary
sefaozalpadl/stop_the_steal_relevancy_analysis-binary
stop_the_steal_relevancy_analysis-binary 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: 23995359 - CO2 Emissions (in grams): 0.6503024714880831
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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
- e3f918a2
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831
Validation Metrics
- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032
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/stop_the_steal_relevancy_analysis-binary
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("sefaozalpadl/stop_the_steal_relevancy_analysis-binary", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("sefaozalpadl/stop_the_steal_relevancy_analysis-binary", use_auth_token=True)
inputs = tokenizer("take our country back. Stop the steal! #trump2020", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Nov 7, 2021