metadata

tags: autonlp
language: en
widget:
  - text: Not Many People Know About The City 1200 Feet Below Detroit
  - text: >-
      Bob accepts the challenge, and the next week they're standing in Saint
      Peters square. 'This isnt gonna work, he's never going to see me here when
      theres this much people. You stay here, I'll go talk to him and you'll see
      me on the balcony, the guards know me too.' Half an hour later, Bob and
      the pope appear side by side on the balcony. Bobs boss gets a heart
      attack, and Bob goes to visit him in the hospital.
  - text: >-
      I’m sorry if you made it this far, but I’m just genuinely idk, I feel like
      I shouldn’t give up, it’s just getting harder to come back from stuff like
      this.
datasets:
  - akilesh96/autonlp-data-mrcooper_text_classification
co2_eq_emissions: 5.999771405025692

Model Trained Using AutoNLP

Problem type: Multi-class Classification
Model ID: 529614927
CO2 Emissions (in grams): 5.999771405025692

Validation Metrics

Loss: 0.7582379579544067
Accuracy: 0.7636103151862464
Macro F1: 0.770630619486531
Micro F1: 0.7636103151862464
Weighted F1: 0.765233270165301
Macro Precision: 0.7746285216467107
Micro Precision: 0.7636103151862464
Weighted Precision: 0.7683270753840836
Macro Recall: 0.7680576576961138
Micro Recall: 0.7636103151862464
Weighted Recall: 0.7636103151862464

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/akilesh96/autonlp-mrcooper_text_classification-529614927

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True)

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

outputs = model(**inputs)