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--- |
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tags: |
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- autotrain |
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- text-classification |
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language: |
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- pt |
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widget: |
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- text: "I love AutoTrain 🤗" |
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datasets: |
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- alexandreteles/autotrain-data-told_br_binary_sm |
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co2_eq_emissions: |
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emissions: 4.429755329718354 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Binary Classification |
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- Model ID: 2489276793 |
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- Base model: bert-base-multilingual-cased |
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- Parameters: 109M |
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- Model size: 416MB |
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- CO2 Emissions (in grams): 4.4298 |
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## Validation Metrics |
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- Loss: 0.432 |
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- Accuracy: 0.800 |
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- Precision: 0.823 |
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- Recall: 0.704 |
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- AUC: 0.891 |
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- F1: 0.759 |
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## Usage |
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This model was trained on a random subset of the [told-br](https://huggingface.co/datasets/told-br) dataset (1/3 of the original size). Our main objective is to provide a small |
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model that can be used to classify Brazilian Portuguese tweets in a binary way ('toxic' or 'non toxic'). |
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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/alexandreteles/autotrain-told_br_binary_sm-2489276793 |
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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("alexandreteles/told_br_binary_sm") |
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tokenizer = AutoTokenizer.from_pretrained("alexandreteles/told_br_binary_sm") |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
