Feature Extraction
Transformers
PyTorch
Safetensors
German
modernbert
fill-mask
masked-lm
long-context
text-embeddings-inference
Instructions to use LSX-UniWue/ModernGBERT_1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSX-UniWue/ModernGBERT_1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LSX-UniWue/ModernGBERT_1B")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/ModernGBERT_1B") model = AutoModelForMaskedLM.from_pretrained("LSX-UniWue/ModernGBERT_1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adjust max length in tokenizer config
Browse filesUpdate tokenizer configuration to align max length with `config.json`. This change does not affect the tokenizer's output. It only prevents (incorrect) warnings for sequences exceeding 1024 tokens.
- tokenizer_config.json +2 -2
tokenizer_config.json
CHANGED
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@@ -46,8 +46,8 @@
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| 46 |
"do_basic_tokenize": true,
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| 47 |
"do_lower_case": false,
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| 48 |
"mask_token": "[MASK]",
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| 49 |
-
"max_len":
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| 50 |
-
"model_max_length":
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| 51 |
"never_split": null,
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| 52 |
"pad_token": "[PAD]",
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| 53 |
"sep_token": "[SEP]",
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|
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| 46 |
"do_basic_tokenize": true,
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| 47 |
"do_lower_case": false,
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| 48 |
"mask_token": "[MASK]",
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| 49 |
+
"max_len": 8192,
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| 50 |
+
"model_max_length": 8192,
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| 51 |
"never_split": null,
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| 52 |
"pad_token": "[PAD]",
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| 53 |
"sep_token": "[SEP]",
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