metadata

tags:
  - autotrain
  - text-classification
  - lam
  - metadata
language:
  - it
widget:
  - text: porta a due battenti.Figure:putti.Animali:aquila.Decorazioni
  - text: Elemento di decorazione architettonica a rilievo
datasets:
  - biglam/cultural_heritage_metadata_accuracy
co2_eq_emissions:
  emissions: 7.171395981202868
metrics:
  - f1
  - accuracy
  - recall
pipeline_tag: text-classification
license: mit
library_name: transformers

Model Card for Cultural Heritage Metadata Accuracy Detection model

This model is trained to detect the quality of Italian cultural heritage metadata, assigning a score of high quality or low quality.

Model Trained Using AutoTrain

Problem type: Multi-class Classification
Model ID: 48840118272
CO2 Emissions (in grams): 7.1714

Validation Metrics

Loss: 0.085
Accuracy: 0.972
Macro F1: 0.972
Micro F1: 0.972
Weighted F1: 0.972
Macro Precision: 0.972
Micro Precision: 0.972
Weighted Precision: 0.972
Macro Recall: 0.972
Micro Recall: 0.972
Weighted Recall: 0.972

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/davanstrien/autotrain-cultural_heritage_metadata_accuracy-48840118272

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("davanstrien/autotrain-cultural_heritage_metadata_accuracy-48840118272", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-cultural_heritage_metadata_accuracy-48840118272", use_auth_token=True)

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

outputs = model(**inputs)