Fill-Mask
Transformers
Safetensors
Urdu
modernbert
urdu
encoder
masked-language-modeling
long-context
8k-context
urblimp
zero-shot
benchmark
Instructions to use ProximaAI/urnova-95m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProximaAI/urnova-95m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ProximaAI/urnova-95m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ProximaAI/urnova-95m") model = AutoModelForMaskedLM.from_pretrained("ProximaAI/urnova-95m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,188 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
}
},
"bos_token": "[CLS]",
"clean_up_tokenization_spaces": false,
"cls_token": "[CLS]",
"eos_token": "[SEP]",
"extra_special_tokens": {},
"mask_token": "[MASK]",
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "[UNK]"
}
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