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--- |
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tags: |
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- autotrain |
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- text-classification |
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language: |
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- en |
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widget: |
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- text: I love AutoTrain 🤗 |
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datasets: |
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- AyoubChLin/autotrain-data-albert-bbc-news |
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- SetFit/bbc-news |
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co2_eq_emissions: |
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emissions: 13.344689233410659 |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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: 48939118438 |
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- CO2 Emissions (in grams): 13.3447 |
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## Validation Metrics |
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- Loss: 0.103 |
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- Accuracy: 0.978 |
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- Macro F1: 0.978 |
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- Micro F1: 0.978 |
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- Weighted F1: 0.978 |
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- Macro Precision: 0.977 |
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- Micro Precision: 0.978 |
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- Weighted Precision: 0.978 |
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- Macro Recall: 0.978 |
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- Micro Recall: 0.978 |
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- Weighted Recall: 0.978 |
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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/AyoubChLin/autotrain-albert-bbc-news-48939118438 |
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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("AyoubChLin/autotrain-albert-bbc-news-48939118438", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-albert-bbc-news-48939118438", 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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``` |
