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autonlp-FR_another_test-565016091
medA/autonlp-FR_another_test-565016091
autonlp-FR_another_test-565016091 is a text classification model from medA. 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: 565016091 - CO2 Emissions (in grams): 70.54639641012226
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- camembert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 11, 2022
- SHA
- c5f7ca05
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 565016091
- CO2 Emissions (in grams): 70.54639641012226
Validation Metrics
- Loss: 0.5170354247093201
- Accuracy: 0.8545909432074056
- Macro F1: 0.7910662503820883
- Micro F1: 0.8545909432074056
- Weighted F1: 0.8539837213761081
- Macro Precision: 0.8033640381948799
- Micro Precision: 0.8545909432074056
- Weighted Precision: 0.856160322286008
- Macro Recall: 0.7841845637031052
- Micro Recall: 0.8545909432074056
- Weighted Recall: 0.8545909432074056
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/medA/autonlp-FR_another_test-565016091
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("medA/autonlp-FR_another_test-565016091", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("medA/autonlp-FR_another_test-565016091", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- camembert
- Languages
- fr
- Created
- Mar 2, 2022
- Updated
- Feb 11, 2022