--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - palakagl/autotrain-data-PersonalAssitant co2_eq_emissions: 0.014567637985425905 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 717221783 - CO2 Emissions (in grams): 0.014567637985425905 ## Validation Metrics - Loss: 0.38848456740379333 - Accuracy: 0.9180509413067552 - Macro F1: 0.9157418163085091 - Micro F1: 0.9180509413067552 - Weighted F1: 0.9185290137253468 - Macro Precision: 0.9189981206383326 - Micro Precision: 0.9180509413067552 - Weighted Precision: 0.9221607328493303 - Macro Recall: 0.9158232837734661 - Micro Recall: 0.9180509413067552 - Weighted Recall: 0.9180509413067552 ## Usage You can use cURL to access this model: ``` $ 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/palakagl/autotrain-PersonalAssitant-717221783 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```