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--- |
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language: |
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- bn |
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metrics: |
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- code_eval |
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- accuracy |
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- bertscore |
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--- |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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irony_name = "raquiba/sarcasm-detection-BanglaSARC" |
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tokenizer_irony = AutoTokenizer.from_pretrained(irony_name) |
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model_irony = AutoModelForSequenceClassification.from_pretrained(irony_name) |
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irony_pipeline = pipeline("sentiment-analysis", model=model_irony, tokenizer=tokenizer_irony, device=0,max_length=512, padding=True, truncation=True) |
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#Model Evaluation |
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tokenizer = AutoTokenizer.from_pretrained(irony_name) |
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df_train, df_test = tokenized_data(df_eval) |
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model_irony = AutoModelForSequenceClassification.from_pretrained(irony_name, num_labels=2, ignore_mismatched_sizes=True).to(device) |
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training_args = TrainingArguments("test-trainer-banglaBERT", {'reprocess_input_data': True}, evaluation_strategy="epoch") |
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trainer_irony.train() |
