readme: ---
  tags: [autonlp, Text Classification, Politics]
  language: fr
  widget:
  - text: "Il y a dans ce pays une fracture"
  datasets:
  - mazancourt/autonlp-data-politics-sentence-classifier
  co2_eq_emissions: 1.06099358268878
  ---
  
  # Prediction of sentence "nature" in a French political sentence
  
  This model aims at predicting the nature of a sentence in a French political sentence.
  The predictions fall in three categories:
  - `problem`: the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce pays une fracture_ (J. Chirac)
  - `solution`: the sentences describes a solution (typically part of a political programme), for example: _J’ai supprimé les droits de succession parce que je crois au travail et parce que je crois à la famille._ (N. Sarkozy)
  - `other`: the sentence does not belong to any of these categories, for example: _vive la République, vive la France_
  
  This model was trained using AutoNLP based on sentences extracted from a mix of political tweets and speeches.
  
  # Model Trained Using AutoNLP
  
  - Problem type: Multi-class Classification
  - Model ID: 23105051
  - CO2 Emissions (in grams): 1.06099358268878
  
  ## Validation Metrics
  
  - Loss: 0.6050735712051392
  - Accuracy: 0.8097826086956522
  - Macro F1: 0.7713543865034599
  - Micro F1: 0.8097826086956522
  - Weighted F1: 0.8065488494385247
  - Macro Precision: 0.7861074705111403
  - Micro Precision: 0.8097826086956522
  - Weighted Precision: 0.806470454156932
  - Macro Recall: 0.7599656456873758
  - Micro Recall: 0.8097826086956522
  - Weighted Recall: 0.8097826086956522
  
  
  ## 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": "Il y a dans ce pays une fracture"}' https://api-inference.huggingface.co/models/mazancourt/politics-sentence-classifier
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("mazancourt/autonlp-politics-sentence-classifier-23105051", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("mazancourt/politics-sentence-classifier", use_auth_token=True)
  
  inputs = tokenizer("Il y a dans ce pays une fracture", return_tensors="pt")
  
  outputs = model(**inputs)
  
  # Category can be "problem", "solution" or "other"
  category = outputs[0]["label"]
  score = outputs[0]["score"]
  ```