Instructions to use hf-tiny-model-private/tiny-random-MarkupLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MarkupLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-MarkupLMModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") - Notebooks
- Google Colab
- Kaggle
File size: 803 Bytes
b8b1298 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"architectures": [
"MarkupLMModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"max_depth": 10,
"max_position_embeddings": 512,
"max_xpath_subs_unit_embeddings": 30,
"max_xpath_tag_unit_embeddings": 20,
"model_type": "markuplm",
"num_attention_heads": 4,
"num_hidden_layers": 5,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"subs_pad_id": 2,
"tag_pad_id": 2,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"type_vocab_size": 16,
"use_cache": true,
"vocab_size": 50267,
"xpath_unit_hidden_size": 32
}
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