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