Token Classification
Transformers
TensorBoard
Safetensors
English
electra
biology
chemistry
medical
cancer
carcinogenesis
biomedical
ner
oncology
Eval Results (legacy)
Instructions to use jimnoneill/CarD-T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimnoneill/CarD-T with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jimnoneill/CarD-T")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jimnoneill/CarD-T") model = AutoModelForTokenClassification.from_pretrained("jimnoneill/CarD-T") - Notebooks
- Google Colab
- Kaggle
File size: 1,361 Bytes
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"added_tokens_decoder": {
"0": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "[CLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "[SEP]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "[MASK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"extra_special_tokens": {},
"ignore_mismatched_sizes": true,
"mask_token": "[MASK]",
"max_len": 512,
"model_max_length": 512,
"never_split": null,
"pad_token": "[PAD]",
"padding": true,
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "ElectraTokenizer",
"truncation": true,
"unk_token": "[UNK]"
}
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