Indonesian Sarcasm Detection
Collection
D. Suhartono, W. Wongso and A. T. Handoyo, "IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection," in IEEE Access. • 14 items • Updated • 1
How to use w11wo/indobert-large-p1-reddit-indonesia-sarcastic with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="w11wo/indobert-large-p1-reddit-indonesia-sarcastic") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("w11wo/indobert-large-p1-reddit-indonesia-sarcastic")
model = AutoModelForSequenceClassification.from_pretrained("w11wo/indobert-large-p1-reddit-indonesia-sarcastic")This model is a fine-tuned version of indobenchmark/indobert-large-p1 on an unknown dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4573 | 1.0 | 309 | 0.4251 | 0.7966 | 0.5684 | 0.6058 | 0.5354 |
| 0.3274 | 2.0 | 618 | 0.4458 | 0.7824 | 0.5955 | 0.5567 | 0.6402 |
| 0.1999 | 3.0 | 927 | 0.5890 | 0.8065 | 0.5412 | 0.6653 | 0.4561 |
| 0.0864 | 4.0 | 1236 | 0.8080 | 0.8023 | 0.5536 | 0.6360 | 0.4901 |
| 0.0391 | 5.0 | 1545 | 1.1299 | 0.7895 | 0.5293 | 0.6007 | 0.4731 |
Base model
indobenchmark/indobert-large-p1