Instructions to use nlpie/bio-mobilebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpie/bio-mobilebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpie/bio-mobilebert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlpie/bio-mobilebert") model = AutoModelForMaskedLM.from_pretrained("nlpie/bio-mobilebert") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "distil-biobert/models/bio-mobilebert/final/model", | |
| "architectures": [ | |
| "MobileBertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_activation": false, | |
| "classifier_dropout": null, | |
| "embedding_size": 128, | |
| "hidden_act": "relu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 512, | |
| "intra_bottleneck_size": 128, | |
| "key_query_shared_bottleneck": true, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "mobilebert", | |
| "normalization_type": "no_norm", | |
| "num_attention_heads": 4, | |
| "num_feedforward_networks": 4, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.17.0", | |
| "trigram_input": true, | |
| "true_hidden_size": 128, | |
| "type_vocab_size": 2, | |
| "use_bottleneck": true, | |
| "use_bottleneck_attention": false, | |
| "vocab_size": 30522 | |
| } | |