readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."
  datasets:
  - Shenzy/autotrain-data-sentence_classification
  co2_eq_emissions: 0.00986494387043499
  ---
  
  ## Validation Metrics
  
  - Loss: 0.6447726488113403
  - Accuracy: 0.8263473053892215
  - Macro F1: 0.7776555055392036
  - Micro F1: 0.8263473053892215
  - Weighted F1: 0.8161511591973788
  - Macro Precision: 0.8273504273504274
  - Micro Precision: 0.8263473053892215
  - Weighted Precision: 0.8266697374481806
  - Macro Recall: 0.7615518744551003
  - Micro Recall: 0.8263473053892215
  - Weighted Recall: 0.8263473053892215
  
  
  ## 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": "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."}' https://api-inference.huggingface.co/models/Shenzy/Sentence_Classification4DesignTutor
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  import numpy as np
  
  labdic ={ 0: "rationale", 1: "suggestion", 2: "specific_critique"}
  
  model = AutoModelForSequenceClassification.from_pretrained("Shenzy/Sentence_Classification4DesignTutor")
  
  tokenizer = AutoTokenizer.from_pretrained("Shenzy/Sentence_Classification4DesignTutor")
  
  inputs = tokenizer("An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name.", return_tensors="pt")
  
  outputs = model(**inputs)
  
  print(labdic[np.argmax(outputs)])
  ```