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--- |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- text |
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- nlp |
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- correction |
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--- |
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# Model Trained Using AutoNLP |
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- Problem type: Multi-class Classification |
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- Model ID: 492513457 |
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- CO2 Emissions (in grams): 5.527544460835904 |
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## Validation Metrics |
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- Loss: 0.07609463483095169 |
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- Accuracy: 0.9735624586913417 |
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- Macro F1: 0.9736173135739408 |
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- Micro F1: 0.9735624586913417 |
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- Weighted F1: 0.9736173135739408 |
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- Macro Precision: 0.9737771415197378 |
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- Micro Precision: 0.9735624586913417 |
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- Weighted Precision: 0.9737771415197378 |
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- Macro Recall: 0.9735624586913417 |
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- Micro Recall: 0.9735624586913417 |
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- Weighted Recall: 0.9735624586913417 |
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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": "Is this text really worth it?"}' https://api-inference.huggingface.co/models/wajidlinux99/gibberish-text-detector |
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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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model = AutoModelForSequenceClassification.from_pretrained("wajidlinux99/gibberish-text-detector", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("wajidlinux99/gibberish-text-detector", use_auth_token=True) |
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inputs = tokenizer("Is this text really worth it?", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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# Original Repository |
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***madhurjindal/autonlp-Gibberish-Detector-492513457 |
