Token Classification
Transformers
Safetensors
English
modernbert
fill-mask
orality
linguistics
multi-label
custom_code
Instructions to use HavelockAI/bert-token-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HavelockAI/bert-token-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HavelockAI/bert-token-classifier", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HavelockAI/bert-token-classifier", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("HavelockAI/bert-token-classifier", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
2641f46 47ff542 2641f46 47ff542 2641f46 47ff542 2641f46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": false,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "TokenizersBackend",
"unk_token": "[UNK]"
}
|