|
--- |
|
license: mit |
|
base_model: microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: results |
|
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. --> |
|
|
|
# results |
|
|
|
This model is a fine-tuned version of [microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL](https://huggingface.co/microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4228 |
|
- Precision: 0.9215 |
|
- Recall: 0.9209 |
|
- Accuracy: 0.9211 |
|
- F1: 0.9210 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:| |
|
| No log | 1.0 | 308 | 0.3266 | 0.8847 | 0.8822 | 0.8820 | 0.8824 | |
|
| 0.4217 | 2.0 | 616 | 0.3034 | 0.9072 | 0.9066 | 0.9064 | 0.9065 | |
|
| 0.4217 | 3.0 | 924 | 0.3483 | 0.9171 | 0.9170 | 0.9170 | 0.9171 | |
|
| 0.163 | 4.0 | 1232 | 0.3952 | 0.9227 | 0.9227 | 0.9227 | 0.9226 | |
|
| 0.0722 | 5.0 | 1540 | 0.4228 | 0.9215 | 0.9209 | 0.9211 | 0.9210 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.2 |
|
|
