Instructions to use answerdotai/ModernBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use answerdotai/ModernBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="answerdotai/ModernBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") model = AutoModelForMaskedLM.from_pretrained("answerdotai/ModernBERT-base") - Notebooks
- Google Colab
- Kaggle
Set tokenizer "model_max_length" property to 8192 (#39)
Browse files- Set tokenizer "model_max_length" property to 8192 (9e1cd05c51ed0a9a9a94ac6e51ccb76ed6d6f3ae)
Co-authored-by: Antoine Chaffin <NohTow@users.noreply.huggingface.co>
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
|
@@ -932,7 +932,7 @@
|
|
| 932 |
"clean_up_tokenization_spaces": true,
|
| 933 |
"cls_token": "[CLS]",
|
| 934 |
"mask_token": "[MASK]",
|
| 935 |
-
"model_max_length":
|
| 936 |
"pad_token": "[PAD]",
|
| 937 |
"sep_token": "[SEP]",
|
| 938 |
"tokenizer_class": "PreTrainedTokenizerFast",
|
|
|
|
| 932 |
"clean_up_tokenization_spaces": true,
|
| 933 |
"cls_token": "[CLS]",
|
| 934 |
"mask_token": "[MASK]",
|
| 935 |
+
"model_max_length": 8192,
|
| 936 |
"pad_token": "[PAD]",
|
| 937 |
"sep_token": "[SEP]",
|
| 938 |
"tokenizer_class": "PreTrainedTokenizerFast",
|