---
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_BIOMAT_NER3
  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_NER3

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3972
- Precision: 0.5228
- Recall: 0.7391
- F1: 0.6124
- Accuracy: 0.9437

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 422  | 0.2590          | 0.4873    | 0.6950 | 0.5729 | 0.9387   |
| 0.2326        | 2.0   | 844  | 0.2598          | 0.5160    | 0.7084 | 0.5971 | 0.9428   |
| 0.0654        | 3.0   | 1266 | 0.3152          | 0.5105    | 0.6936 | 0.5881 | 0.9430   |
| 0.0342        | 4.0   | 1688 | 0.3075          | 0.5214    | 0.7208 | 0.6051 | 0.9432   |
| 0.0208        | 5.0   | 2110 | 0.3623          | 0.5109    | 0.7370 | 0.6034 | 0.9421   |
| 0.0126        | 6.0   | 2532 | 0.3504          | 0.5167    | 0.7139 | 0.5995 | 0.9428   |
| 0.0126        | 7.0   | 2954 | 0.3708          | 0.5260    | 0.7453 | 0.6167 | 0.9445   |
| 0.0073        | 8.0   | 3376 | 0.3898          | 0.5175    | 0.7294 | 0.6054 | 0.9432   |
| 0.0058        | 9.0   | 3798 | 0.3917          | 0.5185    | 0.7391 | 0.6094 | 0.9432   |
| 0.0039        | 10.0  | 4220 | 0.3972          | 0.5228    | 0.7391 | 0.6124 | 0.9437   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1