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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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- abhishek/autonlp-data-bbc-news-classification |
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co2_eq_emissions: 5.448567309047846 |
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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: 37229289 |
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- CO2 Emissions (in grams): 5.448567309047846 |
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## Validation Metrics |
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- Loss: 0.07081354409456253 |
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- Accuracy: 0.9867109634551495 |
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- Macro F1: 0.9859067529980614 |
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- Micro F1: 0.9867109634551495 |
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- Weighted F1: 0.9866417220968429 |
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- Macro Precision: 0.9868771404595043 |
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- Micro Precision: 0.9867109634551495 |
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- Weighted Precision: 0.9869289511551576 |
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- Macro Recall: 0.9853173241852486 |
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- Micro Recall: 0.9867109634551495 |
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- Weighted Recall: 0.9867109634551495 |
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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/abhishek/autonlp-bbc-news-classification-37229289 |
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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("abhishek/autonlp-bbc-news-classification-37229289", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-bbc-news-classification-37229289", 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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``` |
