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--- |
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tags: autotrain |
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language: unk |
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widget: |
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- text: "I love AutoTrain 🤗" |
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datasets: |
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- zainalq7/autotrain-data-NLU_crypto_sentiment_analysis |
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co2_eq_emissions: 0.005300030853867218 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: 754123133 |
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- CO2 Emissions (in grams): 0.005300030853867218 |
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## Validation Metrics |
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- Loss: 0.387116938829422 |
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- Accuracy: 0.8658536585365854 |
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- Macro F1: 0.7724053724053724 |
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- Micro F1: 0.8658536585365854 |
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- Weighted F1: 0.8467166979362101 |
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- Macro Precision: 0.8232219717155155 |
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- Micro Precision: 0.8658536585365854 |
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- Weighted Precision: 0.8516026874759421 |
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- Macro Recall: 0.7642089093701996 |
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- Micro Recall: 0.8658536585365854 |
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- Weighted Recall: 0.8658536585365854 |
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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/zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133 |
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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("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", 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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``` |
