README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - MMars/autotrain-data-marbertv2_flodusta
co2_eq_emissions:
  emissions: 0.026171515160105633

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 2782682135
• CO2 Emissions (in grams): 0.0262

Validation Metrics

• Loss: 0.157
• Accuracy: 0.948
• Macro F1: 0.945
• Micro F1: 0.948
• Weighted F1: 0.948
• Macro Precision: 0.940
• Micro Precision: 0.948
• Weighted Precision: 0.949
• Macro Recall: 0.952
• Micro Recall: 0.948
• Weighted Recall: 0.948

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/MMars/autotrain-marbertv2_flodusta-2782682135

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MMars/autotrain-marbertv2_flodusta-2782682135", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MMars/autotrain-marbertv2_flodusta-2782682135", use_auth_token=True)

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

outputs = model(**inputs)