---
license: mit
base_model: Tsubasaz/clinical-pubmed-bert-base-512
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: 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. -->

# model

This model is a fine-tuned version of [Tsubasaz/clinical-pubmed-bert-base-512](https://huggingface.co/Tsubasaz/clinical-pubmed-bert-base-512) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3511
- Precision: 0.6103
- Recall: 0.5640

## 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: 3e-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| No log        | 1.0   | 128  | 0.4393          | 0.0       | 0.0    |
| No log        | 2.0   | 256  | 0.3958          | 0.5714    | 0.1706 |
| No log        | 3.0   | 384  | 0.3785          | 0.5690    | 0.3128 |
| 0.4046        | 4.0   | 512  | 0.3676          | 0.5789    | 0.5213 |
| 0.4046        | 5.0   | 640  | 0.3606          | 0.6532    | 0.3839 |
| 0.4046        | 6.0   | 768  | 0.3597          | 0.6549    | 0.4408 |
| 0.4046        | 7.0   | 896  | 0.3584          | 0.6376    | 0.4502 |
| 0.3046        | 8.0   | 1024 | 0.3518          | 0.6310    | 0.5024 |
| 0.3046        | 9.0   | 1152 | 0.3511          | 0.6133    | 0.5261 |
| 0.3046        | 10.0  | 1280 | 0.3511          | 0.6103    | 0.5640 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0