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
Upload folder using huggingface_hub
Browse files- modeling_havelock.py +0 -5
modeling_havelock.py
CHANGED
|
@@ -87,11 +87,6 @@ class HavelockTokenConfig(PretrainedConfig):
|
|
| 87 |
"""Config that wraps any backbone config + our custom fields."""
|
| 88 |
|
| 89 |
model_type = "havelock_token_classifier"
|
| 90 |
-
_tied_weights_keys = []
|
| 91 |
-
|
| 92 |
-
def mark_tied_weights_as_initialized(self):
|
| 93 |
-
"""No tied weights in this model."""
|
| 94 |
-
pass
|
| 95 |
|
| 96 |
def __init__(self, num_types: int = 1, use_crf: bool = False, **kwargs):
|
| 97 |
super().__init__(**kwargs)
|
|
|
|
| 87 |
"""Config that wraps any backbone config + our custom fields."""
|
| 88 |
|
| 89 |
model_type = "havelock_token_classifier"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 90 |
|
| 91 |
def __init__(self, num_types: int = 1, use_crf: bool = False, **kwargs):
|
| 92 |
super().__init__(**kwargs)
|