Instructions to use hf-tiny-model-private/tiny-random-LxmertForQuestionAnswering 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-LxmertForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-LxmertForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LxmertForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LxmertForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
File size: 958 Bytes
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"architectures": [
"LxmertForQuestionAnswering"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 28,
"initializer_range": 0.02,
"intermediate_size": 64,
"l_layers": 2,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "lxmert",
"num_attention_heads": 2,
"num_attr_labels": 4,
"num_hidden_layers": {
"cross_encoder": 1,
"language": 2,
"vision": 1
},
"num_object_labels": 16,
"num_qa_labels": 30,
"pad_token_id": 0,
"r_layers": 1,
"task_mask_lm": true,
"task_matched": true,
"task_obj_predict": true,
"task_qa": true,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"type_vocab_size": 2,
"visual_attr_loss": true,
"visual_feat_dim": 128,
"visual_feat_loss": true,
"visual_loss_normalizer": 6.67,
"visual_obj_loss": true,
"visual_pos_dim": 4,
"vocab_size": 1124,
"x_layers": 1
}
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