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pro-cell-expert
Mim/pro-cell-expert
pro-cell-expert is a text classification model from Mim. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 800724769 - CO2 Emissions (in grams): 0.004814823138367317
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Repo size
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 29, 2022
- Updated
- Apr 29, 2022
- SHA
- 904f1325
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317
Validation Metrics
- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259
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/Mim/autotrain-procell-expert-800724769
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Apr 29, 2022
- Updated
- Apr 29, 2022