---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: SkLIP-masked
  results: []
license: mit
datasets:
- joshuachou/SkinCAP
language:
- en
base_model:
- allenai/scibert_scivocab_uncased
- openai/clip-vit-base-patch32
pipeline_tag: feature-extraction
---

<!-- 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. -->

# SkLIPA

SkLIPA (Skin CLIP Anonimised) is a hybrid CLIP model finetuned on the [SkinCAP](https://hf.rst.im/datasets/joshuachou/SkinCAP), a multi-modal dermatology dataset annotated 
with rich medical captions. It is built with a SciBERT text encoder and the pre-trained CLIP-32 vision encoder.

The anonymisation procedure was designed to remove age and gender information form the textual description of each image, replacing them with `[AGE]` and `[GENDER]` tokens.

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.2018        | 1.0   | 57   | 4.1344          |
| 4.1697        | 2.0   | 114  | 4.1298          |
| 4.1668        | 3.0   | 171  | 4.1276          |
| 4.164         | 4.0   | 228  | 4.1263          |
| 4.158         | 5.0   | 285  | 4.1253          |
| 4.1583        | 6.0   | 342  | 4.1246          |
| 4.1569        | 7.0   | 399  | 4.1243          |
| 4.1575        | 8.0   | 456  | 4.1241          |
| 4.1564        | 9.0   | 513  | 4.1240          |
| 4.1604        | 10.0  | 570  | 4.1240          |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1