| --- |
| tags: |
| - text-classification |
| language: |
| - en |
| widget: |
| - text: "Oh, the tragedy!" |
| datasets: |
| - yigitkucuk/sentimentale-dataset |
| co2_eq_emissions: |
| emissions: 0.7402856123778213 |
| --- |
| |
| ## Validation Metrics |
|
|
| - Loss: 0.576 |
| - Accuracy: 0.827 |
| - Macro F1: 0.711 |
| - Micro F1: 0.827 |
| - Weighted F1: 0.827 |
| - Macro Precision: 0.708 |
| - Micro Precision: 0.827 |
| - Weighted Precision: 0.828 |
| - Macro Recall: 0.716 |
| - Micro Recall: 0.827 |
| - Weighted Recall: 0.827 |
| - Problem type: Multi-class Classification |
| - CO2 Emissions (in grams): 0.7403 |
| - Model ID: 3099088026 |
|
|
| ## Use with Python API |
|
|
| ``` |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| model = AutoModelForSequenceClassification.from_pretrained("yigitkucuk/Sentimentale", use_auth_token=True) |
| |
| tokenizer = AutoTokenizer.from_pretrained("yigitkucuk/Sentimentale", use_auth_token=True) |
| |
| inputs = tokenizer("Oh, the tragedy!", return_tensors="pt") |
| |
| outputs = model(**inputs) |
| ``` |
