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autonlp-traffic_nlp_binary-537215209
zwang199/autonlp-traffic_nlp_binary-537215209
autonlp-traffic_nlp_binary-537215209 is a text classification model from zwang199. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 537215209 - CO2 Emissions (in grams): 1.171798205242445
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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
- Jan 28, 2022
- SHA
- d86e0d5a
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445
Validation Metrics
- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325
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/zwang199/autonlp-traffic_nlp_binary-537215209
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)
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
- Jan 28, 2022