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--- |
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tags: autonlp |
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language: en |
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widget: |
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- text: "I love AutoNLP 🤗" |
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datasets: |
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- amansolanki/autonlp-data-Tweet-Sentiment-Extraction |
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co2_eq_emissions: 3.651199395353127 |
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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: 20114061 |
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- CO2 Emissions (in grams): 3.651199395353127 |
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## Validation Metrics |
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- Loss: 0.5046541690826416 |
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- Accuracy: 0.8036219581211093 |
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- Macro F1: 0.807095210403678 |
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- Micro F1: 0.8036219581211093 |
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- Weighted F1: 0.8039634739225368 |
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- Macro Precision: 0.8076842795233988 |
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- Micro Precision: 0.8036219581211093 |
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- Weighted Precision: 0.8052135235094771 |
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- Macro Recall: 0.8075241470527056 |
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- Micro Recall: 0.8036219581211093 |
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- Weighted Recall: 0.8036219581211093 |
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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 AutoNLP"}' https://api-inference.huggingface.co/models/amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 |
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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("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True) |
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inputs = tokenizer("I love AutoNLP", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
