metadata
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
SkLIP
SkLIP (Skin CLIP) is a hybrid CLIP model finetuned on the SkinCAP, a multi-modal dermatology dataset annotated with rich medical captions. It is built witha SciBERT text encoder and the pre-trained CLIP-32 vision encoder.
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