Instructions to use aking11/hyebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aking11/hyebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aking11/hyebert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aking11/hyebert") model = AutoModelForMaskedLM.from_pretrained("aking11/hyebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 666 Bytes
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"_name_or_path": "/dbfs/mnt/gumgum-research-sx/ak_scratch/hyebert/model_2",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 514,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.21.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 30000
}
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