Visual Document Retrieval
Transformers
Safetensors
ColPali
English
ultravbert
fill-mask
ultrabert
vision-language
multimodal
document-retrieval
vidore
custom_code
Instructions to use hf-internal-testing/testing-dummy-UltraVBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/testing-dummy-UltraVBERT with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskedLM processor = AutoProcessor.from_pretrained("hf-internal-testing/testing-dummy-UltraVBERT", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/testing-dummy-UltraVBERT", trust_remote_code=True, device_map="auto") - ColPali
How to use hf-internal-testing/testing-dummy-UltraVBERT with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download configuration_ultravbert.py from hf-internal-testing/testing-dummy-UltraVBERT: direct link, hf CLI and curl.
- Browser
- Download file 950 Bytes
-
https://huggingface.co/hf-internal-testing/testing-dummy-UltraVBERT/resolve/main/configuration_ultravbert.py
- Command line
-
hf download hf://hf-internal-testing/testing-dummy-UltraVBERT/configuration_ultravbert.py
-
curl -L -o configuration_ultravbert.py https://huggingface.co/hf-internal-testing/testing-dummy-UltraVBERT/resolve/main/configuration_ultravbert.py
950 Bytes
| from typing import Literal | |
| from huggingface_hub.dataclasses import strict | |
| from transformers import AutoConfig | |
| from transformers.configuration_utils import PreTrainedConfig, SubConfigSpec | |
| class UltraVBertConfig(PreTrainedConfig): | |
| model_type = "ultravbert" | |
| sub_configs_defaults = { | |
| "text_config": SubConfigSpec(config_class=AutoConfig, model_type="ultrabert"), | |
| "vision_config": SubConfigSpec(config_class=AutoConfig, model_type="siglip_vision_model"), | |
| } | |
| text_config: PreTrainedConfig | dict | None = None | |
| vision_config: PreTrainedConfig | dict | None = None | |
| image_token_id: int = 49191 | |
| pixel_shuffle_factor: int = 4 | |
| initializer_range: float = 0.02 | |
| initializer_cutoff_factor: float = 2.0 | |
| classifier_pooling: Literal["cls", "mean"] = "cls" | |
| classifier_dropout: float | int = 0.0 | |
| tie_word_embeddings: bool = False | |
| connector_norm: bool = True | |
| __all__ = ["UltraVBertConfig"] | |