README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
  - id
widget:
  - text: ini filmnya keren banget
datasets:
  - mkhairil/autotrain-data-text-sentiment-indonlu-smse
co2_eq_emissions:
  emissions: 5.395117116799661
license: apache-2.0

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• fine tuned with indonlp/indonlu dataset. (10000 rows from https://huggingface.co/datasets/indonlp/indonlu/viewer/smsa/train)
• Model ID: 2885384370
• CO2 Emissions (in grams): 5.3951

Validation Metrics

• Loss: 0.270
• Accuracy: 0.900
• Macro F1: 0.866
• Micro F1: 0.900
• Weighted F1: 0.899
• Macro Precision: 0.874
• Micro Precision: 0.900
• Weighted Precision: 0.899
• Macro Recall: 0.859
• Micro Recall: 0.900
• Weighted Recall: 0.900

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/mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True)

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

outputs = model(**inputs)