metadata

tags: autotrain
language: en
widget:
  - text: I love AutoTrain 🤗
datasets:
  - palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 2.258363491829382

Model Trained Using AutoTrain

Problem type: Multi-class Classification
Model ID: 717221781
CO2 Emissions (in grams): 2.258363491829382

Validation Metrics

Loss: 0.38660314679145813
Accuracy: 0.9042081949058693
Macro F1: 0.9079200295131094
Micro F1: 0.9042081949058692
Weighted F1: 0.9052766730963512
Macro Precision: 0.9116101664087508
Micro Precision: 0.9042081949058693
Weighted Precision: 0.9097680514456175
Macro Recall: 0.9080246002936301
Micro Recall: 0.9042081949058693
Weighted Recall: 0.9042081949058693

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-717221781

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)