|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: gbert-large-finetuned-cust18 |
|
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. --> |
|
|
|
# gbert-large-finetuned-cust18 |
|
|
|
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1232 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 0.604 | 1.0 | 391 | 0.3560 | |
|
| 0.3497 | 2.0 | 782 | 0.2838 | |
|
| 0.2812 | 3.0 | 1173 | 0.2484 | |
|
| 0.2452 | 4.0 | 1564 | 0.2232 | |
|
| 0.2253 | 5.0 | 1955 | 0.2240 | |
|
| 0.2202 | 6.0 | 2346 | 0.1993 | |
|
| 0.1922 | 7.0 | 2737 | 0.1747 | |
|
| 0.182 | 8.0 | 3128 | 0.1631 | |
|
| 0.1609 | 9.0 | 3519 | 0.1555 | |
|
| 0.1553 | 10.0 | 3910 | 0.1434 | |
|
| 0.147 | 11.0 | 4301 | 0.1399 | |
|
| 0.144 | 12.0 | 4692 | 0.1340 | |
|
| 0.1307 | 13.0 | 5083 | 0.1319 | |
|
| 0.128 | 14.0 | 5474 | 0.1490 | |
|
| 0.1304 | 15.0 | 5865 | 0.1338 | |
|
| 0.1165 | 16.0 | 6256 | 0.1233 | |
|
| 0.1456 | 17.0 | 6647 | 0.1673 | |
|
| 0.1419 | 18.0 | 7038 | 0.1591 | |
|
| 0.1447 | 19.0 | 7429 | 0.1360 | |
|
| 0.1317 | 20.0 | 7820 | 0.1232 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.1 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
