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transcriber-t5-v8
This model is a fine-tuned version of odunola/transcriber-t5-v7 on the odunola/transcriberv3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1924
Model description
This model, a fine-tuned T5, is designed to pinpoint and extract specific Bible scriptures' chapter and verse from a string of text. It's a major component of a broader project but is also versatile enough for your other applications.
The model's learning is driven by a unique dataset, painstakingly compiled from transcripts of sermons on YouTube and various online platforms. This handpicked, curated data equips the model with a specialized understanding of religious discourse and biblical references.
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: 5e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2215 | 0.23 | 500 | 0.2216 |
| 0.1814 | 0.47 | 1000 | 0.2197 |
| 0.2011 | 0.7 | 1500 | 0.2059 |
| 0.2595 | 0.94 | 2000 | 0.2009 |
| 0.2412 | 1.17 | 2500 | 0.2019 |
| 0.1785 | 1.41 | 3000 | 0.1970 |
| 0.1962 | 1.64 | 3500 | 0.1983 |
| 0.2009 | 1.88 | 4000 | 0.1963 |
| 0.2013 | 2.11 | 4500 | 0.1923 |
| 0.2715 | 2.35 | 5000 | 0.1929 |
| 0.2488 | 2.58 | 5500 | 0.1936 |
| 0.1185 | 2.81 | 6000 | 0.1924 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
