|
--- |
|
license: apache-2.0 |
|
base_model: google/mt5-small |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bengali_news_article_summarization_mt5 |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# bengali_news_article_summarization_mt5 |
|
|
|
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2111 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## 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: 0.001 |
|
- train_batch_size: 20 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 160 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine_with_restarts |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| No log | 0.99 | 83 | 0.8963 | |
|
| No log | 2.0 | 167 | 0.3201 | |
|
| 9.149 | 2.99 | 250 | 0.2583 | |
|
| 9.149 | 3.99 | 334 | 0.2372 | |
|
| 0.3009 | 5.0 | 418 | 0.2298 | |
|
| 0.3009 | 5.99 | 501 | 0.2244 | |
|
| 0.3009 | 7.0 | 585 | 0.2213 | |
|
| 0.2524 | 8.0 | 669 | 0.2163 | |
|
| 0.2524 | 8.99 | 752 | 0.2136 | |
|
| 0.2306 | 10.0 | 836 | 0.2126 | |
|
| 0.2306 | 10.99 | 919 | 0.2117 | |
|
| 0.2176 | 11.99 | 1003 | 0.2120 | |
|
| 0.2176 | 13.0 | 1087 | 0.2116 | |
|
| 0.2176 | 13.99 | 1170 | 0.2111 | |
|
| 0.2119 | 14.89 | 1245 | 0.2111 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
