Instructions to use igorktech/hibial-bert-i3-mlm-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/hibial-bert-i3-mlm-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="igorktech/hibial-bert-i3-mlm-v0.1", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("igorktech/hibial-bert-i3-mlm-v0.1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,010 Bytes
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"_name_or_path": "/content/hier-bert-pytorch/data/hibial-model",
"architectures": [
"HiBiAlBertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"auto_map": {
"AutoConfig": "configuration_hibial.HiBiAlBertConfig",
"AutoModel": "modelling_hibial.HiBiAlBertModel",
"AutoModelForMaskedLM": "modelling_hibial.HiBiAlBertForMaskedLM",
"AutoModelForSequenceClassification": "modelling_hibial.HiBiAlBertForSequenceClassification"
},
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 2048,
"layer_norm_eps": 1e-06,
"max_position_embeddings": 512,
"model_type": "hibial-bert",
"norm_first": true,
"num_attention_heads": 8,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"sep_token_id": 3,
"torch_dtype": "float32",
"transformers_version": "4.33.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
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