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/bert-base-multilingual-cased-reddit-indonesia-sarcastic with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="w11wo/bert-base-multilingual-cased-reddit-indonesia-sarcastic") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("w11wo/bert-base-multilingual-cased-reddit-indonesia-sarcastic")
model = AutoModelForSequenceClassification.from_pretrained("w11wo/bert-base-multilingual-cased-reddit-indonesia-sarcastic")This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4935 | 1.0 | 309 | 0.4739 | 0.7711 | 0.5186 | 0.5472 | 0.4929 |
| 0.4203 | 2.0 | 618 | 0.4527 | 0.7895 | 0.5547 | 0.5892 | 0.5241 |
| 0.3469 | 3.0 | 927 | 0.5105 | 0.7923 | 0.4957 | 0.6316 | 0.4079 |
| 0.2754 | 4.0 | 1236 | 0.5126 | 0.7746 | 0.5254 | 0.5552 | 0.4986 |
| 0.2208 | 5.0 | 1545 | 0.6012 | 0.7803 | 0.5064 | 0.5782 | 0.4504 |
Base model
google-bert/bert-base-multilingual-cased