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autonlp-ctrip-653519223
doctorlan/autonlp-ctrip-653519223
autonlp-ctrip-653519223 is a text classification model from doctorlan. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 653519223 - CO2 Emissions (in grams): 24.879856894708393
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Repo size
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 21, 2022
- Updated
- Mar 21, 2022
- SHA
- 43a0bfa0
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Datasets
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653519223
- CO2 Emissions (in grams): 24.879856894708393
Validation Metrics
- Loss: 0.14671853184700012
- Accuracy: 0.9676666666666667
- Precision: 0.9794159885112494
- Recall: 0.9742857142857143
- AUC: 0.9901396825396825
- F1: 0.9768441155407017
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/doctorlan/autonlp-ctrip-653519223
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("doctorlan/autonlp-ctrip-653519223", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("doctorlan/autonlp-ctrip-653519223", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Mar 21, 2022
- Updated
- Mar 21, 2022