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cuad_contract_type
agnihotri/cuad_contract_type
cuad_contract_type is a text classification model from agnihotri. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 809725368 - CO2 Emissions (in grams): 0.07610944071640048
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- May 1, 2022
- Updated
- May 1, 2022
- SHA
- 1df1dd3a
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048
Validation Metrics
- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761
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/agnihotri/autotrain-contract_type-809725368
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- May 1, 2022
- Updated
- May 1, 2022