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---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: BiomedNLP-CIViC-evidence-level-finetuned
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. -->
# BiomedNLP-CIViC-evidence-level-finetuned
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5745
- F1: 0.8312
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 73 | 1.5464 | 0.8260 |
| No log | 2.0 | 146 | 1.7265 | 0.7844 |
| No log | 3.0 | 219 | 1.6166 | 0.8260 |
| No log | 4.0 | 292 | 1.5619 | 0.8260 |
| No log | 5.0 | 365 | 1.5745 | 0.8312 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.21.0
- Tokenizers 0.15.0
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