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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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- en |
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widget: |
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- text: I love AutoTrain 🤗 |
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datasets: |
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- librarian-bots/model_card_dataset_mentions |
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co2_eq_emissions: |
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emissions: 0.12753465619151655 |
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license: mit |
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library_name: transformers |
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pipeline_tag: text-classification |
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metrics: |
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- f1 |
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- accuracy |
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- recall |
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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: 3522695252 |
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- CO2 Emissions (in grams): 0.1275 |
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## Validation Metrics |
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- Loss: 0.000 |
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- Accuracy: 1.000 |
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- Precision: 1.000 |
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- Recall: 1.000 |
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- AUC: 1.000 |
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- F1: 1.000 |
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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/davanstrien/autotrain-dataset-mentions-160223-3522695252 |
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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("davanstrien/autotrain-dataset-mentions-160223-3522695252", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-dataset-mentions-160223-3522695252", 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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``` |
