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DistilBERT-tweet-eval-emotion trained using autoNLP

  • Problem type: Multi-class Classification

Validation Metrics

  • Loss: 0.5564454197883606
  • Accuracy: 0.8057705840957072
  • Macro F1: 0.7536021792986777
  • Micro F1: 0.8057705840957073
  • Weighted F1: 0.8011390170248318
  • Macro Precision: 0.7817458823222652
  • Micro Precision: 0.8057705840957072
  • Weighted Precision: 0.8025156844840151
  • Macro Recall: 0.7369154685020982
  • Micro Recall: 0.8057705840957072
  • Weighted Recall: 0.8057705840957072

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": "Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry"}' https://api-inference.huggingface.co/models/philschmid/autonlp-tweet_eval_vs_comprehend-3092245

Or Python API:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = 'philschmid/DistilBERT-tweet-eval-emotion'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier("Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry")
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Text Classification
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Examples
This model can be loaded on the Inference API on-demand.

Dataset used to train philschmid/DistilBERT-tweet-eval-emotion

Evaluation results