library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
datasets:
- Norod78/HebrewLyricsDataet
language:
- he
base_model:
- avichr/hebEMO_fear
pipeline_tag: text-classification
model-index:
- name: hebrew_lyrics_to_singer_classifer_small_dataset
results: []
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 = {'诪转讬 讻住驻讬': 0, '转讬住诇诐': 1, '拽讜专讬谉 讗诇讗诇': 2, '讗讬驻讛 讛讬诇讚': 3, '讛诪讻砖驻讜转': 4, '谞讜砖讗讬 讛诪讙讘注转': 5, '专讬拽讬 讙诇': 6, '讝拽谞讬 爪驻转': 7, '讘专讬 住讞专讜祝': 8, '讗讛讜讚 讘谞讗讬': 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 = ['诪转讬 讻住驻讬' , '转讬住诇诐', '拽讜专讬谉 讗诇讗诇', '讗讬驻讛 讛讬诇讚', '讛诪讻砖驻讜转', '谞讜砖讗讬 讛诪讙讘注转', '专讬拽讬 讙诇', '讝拽谞讬 爪驻转', '讘专讬 住讞专讜祝', '讗讛讜讚 讘谞讗讬']
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 = "爪讬驻讜专讬诐 诪住转讜讘讘讜转 砖诪讞 驻讛 讜砖诪讞 砖诐"
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)
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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7106 | 1.0 | 5393 | 0.1266 | 0.9643 |
| 0.2078 | 2.0 | 10786 | 0.0621 | 0.9861 |
| 0.1516 | 3.0 | 16179 | 0.0246 | 0.9945 |
| 0.1018 | 4.0 | 21572 | 0.0114 | 0.9978 |
| 0.0554 | 5.0 | 26965 | 0.0080 | 0.9987 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0