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)