Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
exbert
linkbert
biolinkbert
fill-mask
question-answering
text-classification
token-classification
text-embeddings-inference
Instructions to use dimfeld/BioLinkBERT-large-feat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dimfeld/BioLinkBERT-large-feat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dimfeld/BioLinkBERT-large-feat")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dimfeld/BioLinkBERT-large-feat") model = AutoModel.from_pretrained("dimfeld/BioLinkBERT-large-feat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |