| --- |
| 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)]) |
| ``` |
