Instructions to use hf-internal-testing/tiny-random-TvpModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-TvpModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-TvpModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-TvpModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-TvpModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,121 Bytes
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"_valid_processor_keys": [
"videos",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_pad",
"pad_size",
"constant_values",
"pad_mode",
"do_normalize",
"do_flip_channel_order",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format"
],
"constant_values": 0,
"crop_size": {
"height": 448,
"width": 448
},
"do_center_crop": false,
"do_flip_channel_order": true,
"do_normalize": true,
"do_pad": true,
"do_padding": true,
"do_rescale": false,
"do_resize": true,
"image_mean": [
8.2381,
7.3115,
6.6981
],
"image_processor_type": "TvpImageProcessor",
"image_std": [
9.6335,
9.0659,
8.7213
],
"pad_mode": "constant",
"pad_size": {
"height": 448,
"width": 448
},
"padding_size": {
"height": 448,
"width": 448
},
"processor_class": "TvpProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 448
},
"tokenizer": "bert-base-uncased"
}
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