metadata
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)])