|
--- |
|
base_model: allenai/scibert_scivocab_uncased |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: nlp_te_mlm_scibert |
|
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. --> |
|
|
|
# nlp_te_mlm_scibert |
|
|
|
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1478 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 5678 |
|
- gradient_accumulation_steps: 16 |
|
- total_train_batch_size: 256 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 500 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-------:|:----:|:---------------:| |
|
| 1.3828 | 0.9963 | 152 | 1.2566 | |
|
| 1.3087 | 1.9992 | 305 | 1.2295 | |
|
| 1.289 | 2.9955 | 457 | 1.2237 | |
|
| 1.262 | 3.9984 | 610 | 1.2054 | |
|
| 1.2516 | 4.9947 | 762 | 1.1999 | |
|
| 1.229 | 5.9975 | 915 | 1.1944 | |
|
| 1.2272 | 6.9939 | 1067 | 1.1880 | |
|
| 1.2066 | 7.9967 | 1220 | 1.1879 | |
|
| 1.1991 | 8.9996 | 1373 | 1.1807 | |
|
| 1.1978 | 9.9959 | 1525 | 1.1760 | |
|
| 1.1803 | 10.9988 | 1678 | 1.1724 | |
|
| 1.1819 | 11.9951 | 1830 | 1.1716 | |
|
| 1.1659 | 12.9980 | 1983 | 1.1731 | |
|
| 1.1658 | 13.9943 | 2135 | 1.1673 | |
|
| 1.1524 | 14.9971 | 2288 | 1.1669 | |
|
| 1.1481 | 16.0 | 2441 | 1.1590 | |
|
| 1.1468 | 16.9963 | 2593 | 1.1626 | |
|
| 1.1361 | 17.9992 | 2746 | 1.1623 | |
|
| 1.1371 | 18.9955 | 2898 | 1.1582 | |
|
| 1.125 | 19.9984 | 3051 | 1.1540 | |
|
| 1.1276 | 20.9947 | 3203 | 1.1551 | |
|
| 1.1143 | 21.9975 | 3356 | 1.1518 | |
|
| 1.118 | 22.9939 | 3508 | 1.1550 | |
|
| 1.104 | 23.9967 | 3661 | 1.1525 | |
|
| 1.1011 | 24.9996 | 3814 | 1.1483 | |
|
| 1.1061 | 25.9959 | 3966 | 1.1533 | |
|
| 1.0941 | 26.9988 | 4119 | 1.1473 | |
|
| 1.0951 | 27.9951 | 4271 | 1.1444 | |
|
| 1.0866 | 28.9980 | 4424 | 1.1462 | |
|
| 1.089 | 29.9943 | 4576 | 1.1453 | |
|
| 1.0768 | 30.9971 | 4729 | 1.1496 | |
|
| 1.0744 | 32.0 | 4882 | 1.1493 | |
|
| 1.0773 | 32.9963 | 5034 | 1.1478 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.2.1 |
|
- Datasets 2.19.2 |
|
- Tokenizers 0.19.1 |
|
|
