Instructions to use line-corporation/clip-japanese-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use line-corporation/clip-japanese-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="line-corporation/clip-japanese-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("line-corporation/clip-japanese-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "./lycorp/clyp-eva02-b-16", | |
| "architectures": [ | |
| "CLYPModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_clyp.CLYPConfig", | |
| "AutoModel": "modeling_clyp.CLYPModel" | |
| }, | |
| "itc_loss_config": null, | |
| "learn_temperature": true, | |
| "model_type": "clyp", | |
| "temperature_init": 0.07, | |
| "temperature_max": 1000.0, | |
| "temperature_min": 0.01, | |
| "text_encoder_config": { | |
| "backbone_config": { | |
| "model_name": "rinna/japanese-clip-vit-b-16" | |
| }, | |
| "neck_config": { | |
| "bias": false, | |
| "in_channels": 768, | |
| "out_channels": 512 | |
| }, | |
| "pooler_config": { | |
| "input_type": "huggingface", | |
| "return_patch_features": false | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.1", | |
| "vision_encoder_config": { | |
| "backbone_config": { | |
| "extra_kwargs": {}, | |
| "model_name": "eva02_base_patch16_clip_224.merged2b", | |
| "pretrained": true | |
| }, | |
| "neck_config": { | |
| "bias": false, | |
| "in_channels": 768, | |
| "out_channels": 512 | |
| }, | |
| "pooler_config": { | |
| "input_type": "timm", | |
| "return_patch_features": false | |
| } | |
| } | |
| } | |