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--- |
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tags: |
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- autotrain |
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- token-classification |
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- medical |
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language: |
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- fr |
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widget: |
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- text: Prendré 2 compris par jour, pendant 1 mois. |
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- text: DOLIPRANETABS 1000 MG CPR PELL PLQ/8 (Paracétamol 1.000mg comprimé) |
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datasets: |
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- Posos/MedNERF |
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co2_eq_emissions: |
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emissions: 0.11647938304211661 |
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license: mit |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Entity Extraction |
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- Model ID: 69856137957 |
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- CO2 Emissions (in grams): 0.1165 |
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## Validation Metrics |
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- Loss: 1.510 |
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- Accuracy: 0.706 |
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- Precision: 0.648 |
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- Recall: 0.679 |
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- F1: 0.663 |
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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/davanstrien/autotrain-french-ner-blank-model-69856137957 |
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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("davanstrien/autotrain-french-ner-blank-model-69856137957", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-french-ner-blank-model-69856137957", 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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``` |
