Instructions to use amazon/bort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amazon/bort with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="amazon/bort")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("amazon/bort") model = AutoModelForMaskedLM.from_pretrained("amazon/bort", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 507 Bytes
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"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 768,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 8,
"num_hidden_layers": 4,
"pad_token_id": 1,
"tokenizer_class": "RobertaTokenizer",
"type_vocab_size": 1,
"vocab_size": 50265
}
|