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---
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
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