README.md

metadata

tags:
  - autonlp
language: en
widget:
  - text: I love AutoNLP 🤗
datasets:
  - amansolanki/autonlp-data-Tweet-Sentiment-Extraction
co2_eq_emissions: 3.651199395353127

Model Trained Using AutoNLP

• Problem type: Multi-class Classification
• Model ID: 20114061
• CO2 Emissions (in grams): 3.651199395353127

Validation Metrics

• Loss: 0.5046541690826416
• Accuracy: 0.8036219581211093
• Macro F1: 0.807095210403678
• Micro F1: 0.8036219581211093
• Weighted F1: 0.8039634739225368
• Macro Precision: 0.8076842795233988
• Micro Precision: 0.8036219581211093
• Weighted Precision: 0.8052135235094771
• Macro Recall: 0.8075241470527056
• Micro Recall: 0.8036219581211093
• Weighted Recall: 0.8036219581211093

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 AutoNLP"}' https://api-inference.huggingface.co/models/amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)

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

outputs = model(**inputs)