Instructions to use bencyc1129/bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bencyc1129/bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bencyc1129/bert-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bencyc1129/bert-small") model = AutoModelForMaskedLM.from_pretrained("bencyc1129/bert-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 639 Bytes
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"_name_or_path": "bert-small/checkpoint-160000",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"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-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 8,
"num_hidden_layers": 4,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.38.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 100000
}
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