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library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Final_ManufacturedObjects_STL_model
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. -->
# Final_ManufacturedObjects_STL_model
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Precision: 0.9873
- Recall: 0.9852
- F1: 0.9863
- Accuracy: 0.9825
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1069 | 1.0 | 523 | 0.0714 | 0.9823 | 0.9791 | 0.9807 | 0.9758 |
| 0.0411 | 2.0 | 1046 | 0.0689 | 0.9873 | 0.9827 | 0.9850 | 0.9810 |
| 0.0221 | 3.0 | 1569 | 0.0726 | 0.9864 | 0.9845 | 0.9854 | 0.9812 |
| 0.0132 | 4.0 | 2092 | 0.0867 | 0.9870 | 0.9856 | 0.9863 | 0.9820 |
| 0.0081 | 5.0 | 2615 | 0.0973 | 0.9868 | 0.9853 | 0.9861 | 0.9820 |
| 0.0042 | 6.0 | 3138 | 0.1079 | 0.9875 | 0.9852 | 0.9863 | 0.9823 |
| 0.0031 | 7.0 | 3661 | 0.1179 | 0.9881 | 0.9856 | 0.9868 | 0.9825 |
| 0.0016 | 8.0 | 4184 | 0.1146 | 0.9881 | 0.9859 | 0.9870 | 0.9833 |
| 0.0013 | 9.0 | 4707 | 0.1190 | 0.9879 | 0.9856 | 0.9867 | 0.9831 |
| 0.0009 | 10.0 | 5230 | 0.1199 | 0.9873 | 0.9852 | 0.9863 | 0.9825 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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