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---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: run1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Accuracy
type: accuracy
value: 0.595
- name: Precision
type: precision
value: 0.632109581421221
- name: Recall
type: recall
value: 0.595
- name: F1
type: f1
value: 0.5644107445349681
---
<!-- 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. -->
# run1
This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6989
- Accuracy: 0.595
- Precision: 0.6321
- Recall: 0.595
- F1: 0.5644
## 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: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.99 | 53 | 0.6932 | 0.5 | 0.5 | 0.5 | 0.4302 |
| 0.6952 | 2.0 | 107 | 0.6946 | 0.505 | 0.5059 | 0.505 | 0.4854 |
| 0.6952 | 2.99 | 160 | 0.6938 | 0.485 | 0.4127 | 0.485 | 0.3505 |
| 0.6953 | 4.0 | 214 | 0.6937 | 0.5 | 0.5 | 0.5 | 0.4389 |
| 0.6953 | 4.99 | 267 | 0.6961 | 0.5 | 0.25 | 0.5 | 0.3333 |
| 0.6937 | 6.0 | 321 | 0.6936 | 0.5 | 0.25 | 0.5 | 0.3333 |
| 0.6937 | 6.99 | 374 | 0.6908 | 0.495 | 0.4487 | 0.495 | 0.3479 |
| 0.6927 | 8.0 | 428 | 0.6804 | 0.545 | 0.5485 | 0.545 | 0.5366 |
| 0.6927 | 8.99 | 481 | 0.6888 | 0.525 | 0.5535 | 0.525 | 0.4520 |
| 0.6799 | 10.0 | 535 | 0.6657 | 0.615 | 0.6476 | 0.615 | 0.5925 |
| 0.6799 | 10.99 | 588 | 0.6600 | 0.625 | 0.6448 | 0.625 | 0.6117 |
| 0.6509 | 12.0 | 642 | 0.6598 | 0.595 | 0.6407 | 0.595 | 0.5592 |
| 0.6509 | 12.99 | 695 | 0.6598 | 0.605 | 0.6555 | 0.605 | 0.5701 |
| 0.6122 | 14.0 | 749 | 0.6643 | 0.59 | 0.6234 | 0.59 | 0.5603 |
| 0.6122 | 14.99 | 802 | 0.6754 | 0.605 | 0.6818 | 0.605 | 0.5584 |
| 0.5601 | 16.0 | 856 | 0.6788 | 0.605 | 0.6382 | 0.605 | 0.5798 |
| 0.5601 | 16.99 | 909 | 0.6864 | 0.59 | 0.6234 | 0.59 | 0.5603 |
| 0.5159 | 18.0 | 963 | 0.6967 | 0.6 | 0.6457 | 0.6 | 0.5660 |
| 0.5159 | 18.99 | 1016 | 0.7037 | 0.6 | 0.6507 | 0.6 | 0.5633 |
| 0.5117 | 19.81 | 1060 | 0.6989 | 0.595 | 0.6321 | 0.595 | 0.5644 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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