---
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: defect-classification-scibert-baseline-25-epochs
  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. -->

# defect-classification-scibert-baseline-25-epochs

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: 0.2317
- Accuracy: 0.9075

## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3655        | 1.0   | 2124  | 1.1784          | 0.8208   |
| 0.9439        | 2.0   | 4248  | 0.6611          | 0.8474   |
| 0.7364        | 3.0   | 6372  | 0.5215          | 0.8561   |
| 0.6355        | 4.0   | 8496  | 0.4250          | 0.8669   |
| 0.5592        | 5.0   | 10620 | 0.3694          | 0.8740   |
| 0.5411        | 6.0   | 12744 | 0.3482          | 0.8754   |
| 0.5056        | 7.0   | 14868 | 0.3418          | 0.8757   |
| 0.4838        | 8.0   | 16992 | 0.2492          | 0.9132   |
| 0.469         | 9.0   | 19116 | 0.3220          | 0.8785   |
| 0.4468        | 10.0  | 21240 | 0.2693          | 0.8964   |
| 0.432         | 11.0  | 23364 | 0.2756          | 0.8913   |
| 0.4256        | 12.0  | 25488 | 0.2545          | 0.9019   |
| 0.4158        | 13.0  | 27612 | 0.2431          | 0.9061   |
| 0.3961        | 14.0  | 29736 | 0.2532          | 0.9001   |
| 0.3972        | 15.0  | 31860 | 0.2441          | 0.9025   |
| 0.3931        | 16.0  | 33984 | 0.2456          | 0.9008   |
| 0.3882        | 17.0  | 36108 | 0.2536          | 0.8971   |
| 0.3846        | 18.0  | 38232 | 0.2421          | 0.9029   |
| 0.3806        | 19.0  | 40356 | 0.2547          | 0.8960   |
| 0.3761        | 20.0  | 42480 | 0.2527          | 0.8956   |
| 0.3648        | 21.0  | 44604 | 0.2475          | 0.8979   |
| 0.3684        | 22.0  | 46728 | 0.2333          | 0.9079   |
| 0.3679        | 23.0  | 48852 | 0.2300          | 0.9095   |
| 0.3683        | 24.0  | 50976 | 0.2347          | 0.9060   |
| 0.3632        | 25.0  | 53100 | 0.2317          | 0.9075   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0