Instructions to use malteos/PubMedNCL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malteos/PubMedNCL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/PubMedNCL")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/PubMedNCL") model = AutoModel.from_pretrained("malteos/PubMedNCL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 758 Bytes
18ee1d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"_name_or_path": "data/s2orc_with_specter_without_scidocs/specter/corpus_seed_0/seed_0_ep5knn20-25_en3random_without_knn_hn2knn3998-4000/model_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"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": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.5.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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