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---
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_BIOMAT_NER2
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. -->
# MatSciBERT_BIOMAT_NER2
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: 0.5936
- Precision: 0.9555
- Recall: 0.9490
- F1: 0.9523
- Accuracy: 0.9426
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0537 | 1.0 | 1171 | 0.3242 | 0.9568 | 0.9456 | 0.9512 | 0.9406 |
| 0.0163 | 2.0 | 2342 | 0.3726 | 0.9578 | 0.9499 | 0.9538 | 0.9447 |
| 0.0054 | 3.0 | 3513 | 0.4411 | 0.9565 | 0.9496 | 0.9530 | 0.9432 |
| 0.0029 | 4.0 | 4684 | 0.4982 | 0.9562 | 0.9480 | 0.9521 | 0.9420 |
| 0.0017 | 5.0 | 5855 | 0.5276 | 0.9548 | 0.9472 | 0.9510 | 0.9404 |
| 0.001 | 6.0 | 7026 | 0.5463 | 0.9562 | 0.9494 | 0.9528 | 0.9429 |
| 0.0005 | 7.0 | 8197 | 0.5741 | 0.9567 | 0.9500 | 0.9533 | 0.9437 |
| 0.0004 | 8.0 | 9368 | 0.5979 | 0.9557 | 0.9494 | 0.9526 | 0.9428 |
| 0.0003 | 9.0 | 10539 | 0.6028 | 0.9558 | 0.9496 | 0.9527 | 0.9428 |
| 0.0004 | 10.0 | 11710 | 0.5936 | 0.9555 | 0.9490 | 0.9523 | 0.9426 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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