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--- |
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tags: autonlp |
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language: ar |
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widget: |
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- text: "I love AutoNLP 🤗" |
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datasets: |
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- adelgasmi/autonlp-data-kpmg_nlp |
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co2_eq_emissions: 64.58945483765274 |
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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: 18833547 |
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- CO2 Emissions (in grams): 64.58945483765274 |
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## Validation Metrics |
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- Loss: 0.14247722923755646 |
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- Accuracy: 0.9586074193404036 |
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- Macro F1: 0.9468339778730883 |
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- Micro F1: 0.9586074193404036 |
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- Weighted F1: 0.9585551117678807 |
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- Macro Precision: 0.9445436604001405 |
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- Micro Precision: 0.9586074193404036 |
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- Weighted Precision: 0.9591405429662925 |
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- Macro Recall: 0.9499427161888565 |
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- Micro Recall: 0.9586074193404036 |
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- Weighted Recall: 0.9586074193404036 |
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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/adelgasmi/autonlp-kpmg_nlp-18833547 |
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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("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", 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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``` |
