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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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- kSaluja/autonlp-data-tele_red_data_model |
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co2_eq_emissions: 2.379476355147211 |
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--- |
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# Model Trained Using AutoNLP |
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- Problem type: Entity Extraction |
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- Model ID: 585716433 |
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- CO2 Emissions (in grams): 2.379476355147211 |
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## Validation Metrics |
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- Loss: 0.15210922062397003 |
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- Accuracy: 0.9724770642201835 |
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- Precision: 0.950836820083682 |
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- Recall: 0.9625838333921638 |
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- F1: 0.9566742676723382 |
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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/kSaluja/autonlp-tele_red_data_model-585716433 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("kSaluja/autonlp-tele_red_data_model-585716433", 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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``` |
