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-base-uncased-twitter-indonesia-sarcastic with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="w11wo/indobert-base-uncased-twitter-indonesia-sarcastic") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("w11wo/indobert-base-uncased-twitter-indonesia-sarcastic")
model = AutoModelForSequenceClassification.from_pretrained("w11wo/indobert-base-uncased-twitter-indonesia-sarcastic")This model is a fine-tuned version of indolem/indobert-base-uncased 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 |
|---|---|---|---|---|---|---|---|
| 0.5531 | 1.0 | 59 | 0.4977 | 0.7724 | 0.4078 | 0.5833 | 0.3134 |
| 0.4992 | 2.0 | 118 | 0.4785 | 0.7724 | 0.3441 | 0.6154 | 0.2388 |
| 0.44 | 3.0 | 177 | 0.4819 | 0.7799 | 0.3656 | 0.6538 | 0.2537 |
| 0.3815 | 4.0 | 236 | 0.4524 | 0.8097 | 0.6623 | 0.5952 | 0.7463 |
| 0.3104 | 5.0 | 295 | 0.4547 | 0.8172 | 0.5421 | 0.725 | 0.4328 |
| 0.2592 | 6.0 | 354 | 0.4058 | 0.8172 | 0.5664 | 0.6957 | 0.4776 |
| 0.2083 | 7.0 | 413 | 0.4358 | 0.8060 | 0.5738 | 0.6364 | 0.5224 |
Base model
indolem/indobert-base-uncased