File size: 2,358 Bytes
fe9fa9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ST_MAT
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. -->
# ST_MAT
This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Precision: 0.8250
- Recall: 0.8333
- F1: 0.8291
- Accuracy: 0.9766
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1259 | 1.0 | 569 | 0.0862 | 0.8117 | 0.7998 | 0.8057 | 0.9742 |
| 0.0476 | 2.0 | 1138 | 0.0909 | 0.8065 | 0.8154 | 0.8109 | 0.9741 |
| 0.0296 | 3.0 | 1707 | 0.1032 | 0.8039 | 0.8232 | 0.8134 | 0.9739 |
| 0.0196 | 4.0 | 2276 | 0.1157 | 0.8054 | 0.8203 | 0.8128 | 0.9745 |
| 0.0118 | 5.0 | 2845 | 0.1182 | 0.8300 | 0.8311 | 0.8305 | 0.9768 |
| 0.0074 | 6.0 | 3414 | 0.1399 | 0.8204 | 0.8151 | 0.8178 | 0.9753 |
| 0.0053 | 7.0 | 3983 | 0.1445 | 0.8334 | 0.8223 | 0.8278 | 0.9765 |
| 0.0025 | 8.0 | 4552 | 0.1521 | 0.8218 | 0.8288 | 0.8253 | 0.9758 |
| 0.0023 | 9.0 | 5121 | 0.1555 | 0.8215 | 0.8255 | 0.8235 | 0.9759 |
| 0.0016 | 10.0 | 5690 | 0.1551 | 0.8250 | 0.8333 | 0.8291 | 0.9766 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|