---
language:
- bn
metrics:
- code_eval
- accuracy
- bertscore
---

from transformers import AutoTokenizer, AutoModelForSequenceClassification
irony_name = "raquiba/sarcasm-detection-BanglaSARC"
tokenizer_irony = AutoTokenizer.from_pretrained(irony_name)
model_irony = AutoModelForSequenceClassification.from_pretrained(irony_name)
irony_pipeline = pipeline("sentiment-analysis", model=model_irony, tokenizer=tokenizer_irony, device=0,max_length=512, padding=True, truncation=True)

#Model Evaluation
tokenizer = AutoTokenizer.from_pretrained(irony_name)
df_train, df_test = tokenized_data(df_eval)
model_irony = AutoModelForSequenceClassification.from_pretrained(irony_name, num_labels=2, ignore_mismatched_sizes=True).to(device)
training_args = TrainingArguments("test-trainer-banglaBERT", {'reprocess_input_data': True}, evaluation_strategy="epoch")
trainer_irony.train()