hebrew_lyrics_to_singer_classifer_small_dataset

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hebrew_lyrics_to_singer_classifer_small_dataset

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0080
• Accuracy: 0.9987

Model description

This model was trained to classify singer name based on parts of it's song - on a limited 10 singers dataset of Hebrew lyrics, extracted from Norod78/HebrewLyricsDataet 9 singers_list = {'מתי כספי': 0, 'תיסלם': 1, 'קורין אלאל': 2, 'איפה הילד': 3, 'המכשפות': 4, 'נושאי המגבעת': 5, 'ריקי גל': 6, 'זקני צפת': 7, 'ברי סחרוף': 8, 'אהוד בנאי': 9}

Example use

from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

model_path="yaryar78/hebrew_lyrics_to_singer_classifer_small_dataset"

pipe = pipeline("text-classification", model=model_path)

singers_list = ['מתי כספי' , 'תיסלם', 'קורין אלאל', 'איפה הילד', 'המכשפות', 'נושאי המגבעת', 'ריקי גל', 'זקני צפת', 'ברי סחרוף', 'אהוד בנאי']

singers_list = singers_list[::-1]

label2id = {s: i for i, s in enumerate(singers_list)}

id2label = {i: s for s, i in label2id.items()}

prompt = "ציפורים מסתובבות שמח פה ושמח שם"

prediction = pipe(prompt)

label_str = prediction[0]["label"]

print(label_str)

label_id = int(label_str.replace("LABEL_", ""))

predicted_singer = id2label[label_id]

print(predicted_singer)

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Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0