Fill-Mask
Transformers
Safetensors
Sindhi
avey-b
sindhi
masked-language-modeling
encoder
custom_code
Eval Results (legacy)
Instructions to use SndOpenResearch/avey-b-snd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SndOpenResearch/avey-b-snd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SndOpenResearch/avey-b-snd", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("SndOpenResearch/avey-b-snd", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from SndOpenResearch/avey-b-snd: direct link, hf CLI and curl.
- Browser
- Download file 268 Bytes
-
https://huggingface.co/SndOpenResearch/avey-b-snd/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://SndOpenResearch/avey-b-snd/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/SndOpenResearch/avey-b-snd/resolve/main/tokenizer_config.json
268 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]", | |
| "clean_up_tokenization_spaces": false | |
| } | |