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--- |
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tags: autonlp |
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language: fr |
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widget: |
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- text: "I love AutoNLP 🤗" |
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datasets: |
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- medA/autonlp-data-FR_another_test |
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co2_eq_emissions: 70.54639641012226 |
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--- |
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# Model Trained Using AutoNLP |
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- Problem type: Multi-class Classification |
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- Model ID: 565016091 |
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- CO2 Emissions (in grams): 70.54639641012226 |
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## Validation Metrics |
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- Loss: 0.5170354247093201 |
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- Accuracy: 0.8545909432074056 |
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- Macro F1: 0.7910662503820883 |
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- Micro F1: 0.8545909432074056 |
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- Weighted F1: 0.8539837213761081 |
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- Macro Precision: 0.8033640381948799 |
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- Micro Precision: 0.8545909432074056 |
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- Weighted Precision: 0.856160322286008 |
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- Macro Recall: 0.7841845637031052 |
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- Micro Recall: 0.8545909432074056 |
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- Weighted Recall: 0.8545909432074056 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("medA/autonlp-FR_another_test-565016091", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("medA/autonlp-FR_another_test-565016091", use_auth_token=True) |
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inputs = tokenizer("I love AutoNLP", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
