
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# transcriber-t5-v8

This model is a fine-tuned version of [odunola/transcriber-t5-v7](https://huggingface.co/odunola/transcriber-t5-v7) on the [odunola/transcriberv3](https://huggingface.co/datasets/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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "transcriber-t5-v8", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3