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--- |
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tags: autonlp |
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language: en |
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widget: |
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- text: "I love AutoNLP 🤗" |
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datasets: |
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- ds198799/autonlp-data-predict_ROI_1 |
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co2_eq_emissions: 2.7516207978192737 |
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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: 29797722 |
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- CO2 Emissions (in grams): 2.7516207978192737 |
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## Validation Metrics |
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- Loss: 0.6113826036453247 |
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- Accuracy: 0.7559139784946236 |
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- Macro F1: 0.4594734612976928 |
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- Micro F1: 0.7559139784946236 |
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- Weighted F1: 0.7195080232106192 |
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- Macro Precision: 0.7175166413412577 |
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- Micro Precision: 0.7559139784946236 |
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- Weighted Precision: 0.7383048259333735 |
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- Macro Recall: 0.4482203645846237 |
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- Micro Recall: 0.7559139784946236 |
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- Weighted Recall: 0.7559139784946236 |
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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/ds198799/autonlp-predict_ROI_1-29797722 |
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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("ds198799/autonlp-predict_ROI_1-29797722", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("ds198799/autonlp-predict_ROI_1-29797722", 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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``` |
