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--- |
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tags: |
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- autotrain |
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- text-classification |
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- cognitive distortions |
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- psychology |
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- depression |
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language: |
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- unk |
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widget: |
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- text: I love AutoTrain |
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datasets: |
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- halilbabacan/autotrain-data-cognitive_distortions |
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co2_eq_emissions: |
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emissions: 0.8368333755010434 |
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--- |
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The article is under publication. For communication, you can send an e-mail to hakki.babacan@erzincan.edu.tr. |
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# Model Trained Using AutoTrain |
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- Problem type: Binary Classification |
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- Model ID: 73482139269 |
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- CO2 Emissions (in grams): 0.8368 |
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## Validation Metrics |
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- Loss: 0.076 |
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- Accuracy: 0.973 |
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- Precision: 0.912 |
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- Recall: 0.995 |
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- AUC: 0.997 |
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- F1: 0.951 |
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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": "I love AutoTrain"}' https://api-inference.huggingface.co/models/halilbabacan/autotrain-cognitive_distortions-73482139269 |
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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("halilbabacan/autotrain-cognitive_distortions-73482139269", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("halilbabacan/autotrain-cognitive_distortions-73482139269", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
