metadata
tags: autonlp
language: en
widget:
- text: >-
Worry is a down payment on a problem you may never have'. Joyce Meyer.
#motivation #leadership #worry
datasets:
- tweet_eval
model-index:
- name: DistilBERT-tweet-eval-emotion
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name: tweeteval
type: tweet-eval
metrics:
- name: Accuracy
type: accuracy
value: 80.59
- name: Macro F1
type: macro-f1
value: 78.17
- name: Weighted F1
type: weighted-f1
value: 80.11
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")