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--- |
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tags: autotrain |
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language: ar |
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widget: |
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- text: "I love AutoTrain 🤗" |
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datasets: |
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- Yah216/autotrain-data-Poem_Rawiy_detection |
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co2_eq_emissions: 1.8046766441629636 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: 918730150 |
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- CO2 Emissions (in grams): 1.8046766441629636 |
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## Validation Metrics |
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- Loss: 0.398613303899765 |
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- Accuracy: 0.912351981006084 |
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- Macro F1: 0.717311758991278 |
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- Micro F1: 0.912351981006084 |
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- Weighted F1: 0.9110094798809955 |
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- Macro Precision: 0.7211917136609866 |
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- Micro Precision: 0.912351981006084 |
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- Weighted Precision: 0.9102294701380585 |
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- Macro Recall: 0.714852045042265 |
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- Micro Recall: 0.912351981006084 |
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- Weighted Recall: 0.912351981006084 |
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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 AutoTrain"}' https://api-inference.huggingface.co/models/Yah216/autotrain-Poem_Rawiy_detection-918730150 |
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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("Yah216/autotrain-Poem_Rawiy_detection-918730150", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Yah216/autotrain-Poem_Rawiy_detection-918730150", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
