---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: multiCorp_5e-05_LabelNorm_0404
  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. -->

# multiCorp_5e-05_LabelNorm_0404

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: 0.0340
- Precision: 0.6966
- Recall: 0.6039
- F1: 0.6469
- Accuracy: 0.9907

## 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: 5e-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
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2699        | 0.08  | 25   | 0.1037          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0844        | 0.15  | 50   | 0.0821          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0776        | 0.23  | 75   | 0.0761          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0767        | 0.31  | 100  | 0.0680          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0541        | 0.39  | 125  | 0.0602          | 0.6392    | 0.2054 | 0.3110 | 0.9856   |
| 0.0586        | 0.46  | 150  | 0.0553          | 0.7209    | 0.1447 | 0.2411 | 0.9851   |
| 0.051         | 0.54  | 175  | 0.0508          | 0.3651    | 0.4265 | 0.3934 | 0.9837   |
| 0.0325        | 0.62  | 200  | 0.0449          | 0.4337    | 0.3307 | 0.3753 | 0.9858   |
| 0.0429        | 0.7   | 225  | 0.0454          | 0.5562    | 0.4272 | 0.4833 | 0.9874   |
| 0.0357        | 0.77  | 250  | 0.0448          | 0.5351    | 0.3502 | 0.4233 | 0.9855   |
| 0.0542        | 0.85  | 275  | 0.0420          | 0.6611    | 0.2778 | 0.3912 | 0.9872   |
| 0.0396        | 0.93  | 300  | 0.0379          | 0.5781    | 0.4755 | 0.5218 | 0.9882   |
| 0.0354        | 1.01  | 325  | 0.0417          | 0.5083    | 0.6195 | 0.5584 | 0.9877   |
| 0.0326        | 1.08  | 350  | 0.0378          | 0.5295    | 0.4677 | 0.4967 | 0.9875   |
| 0.0277        | 1.16  | 375  | 0.0413          | 0.7018    | 0.3424 | 0.4603 | 0.9882   |
| 0.0429        | 1.24  | 400  | 0.0431          | 0.4924    | 0.5767 | 0.5312 | 0.9869   |
| 0.025         | 1.32  | 425  | 0.0368          | 0.6256    | 0.5331 | 0.5756 | 0.9891   |
| 0.0364        | 1.39  | 450  | 0.0353          | 0.6193    | 0.5354 | 0.5743 | 0.9882   |
| 0.0321        | 1.47  | 475  | 0.0366          | 0.6695    | 0.4949 | 0.5691 | 0.9895   |
| 0.0238        | 1.55  | 500  | 0.0340          | 0.5968    | 0.5230 | 0.5574 | 0.9895   |
| 0.0289        | 1.63  | 525  | 0.0320          | 0.6191    | 0.5907 | 0.6045 | 0.9900   |
| 0.0272        | 1.7   | 550  | 0.0325          | 0.5938    | 0.6257 | 0.6093 | 0.9898   |
| 0.028         | 1.78  | 575  | 0.0316          | 0.6309    | 0.6    | 0.6151 | 0.9903   |
| 0.025         | 1.86  | 600  | 0.0364          | 0.6781    | 0.3852 | 0.4913 | 0.9891   |
| 0.0235        | 1.93  | 625  | 0.0323          | 0.6543    | 0.6467 | 0.6505 | 0.9903   |
| 0.0256        | 2.01  | 650  | 0.0320          | 0.7196    | 0.5292 | 0.6099 | 0.9904   |
| 0.0182        | 2.09  | 675  | 0.0337          | 0.6773    | 0.6436 | 0.6600 | 0.9910   |
| 0.0179        | 2.17  | 700  | 0.0319          | 0.6592    | 0.6218 | 0.6400 | 0.9905   |
| 0.0171        | 2.24  | 725  | 0.0340          | 0.6966    | 0.6039 | 0.6469 | 0.9907   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3