Instructions to use Angshul/SpliNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/SpliNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Angshul/SpliNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Angshul/SpliNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 370 Bytes
4ec5e47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"tokenizer_class": "SpliNetTokenizer",
"auto_map": {
"AutoTokenizer": [
"tokenization_splinet.SpliNetTokenizer",
null
]
},
"model_max_length": 512,
"do_lower_case": true,
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<pad>",
"cls_token": "<cls>",
"sep_token": "<sep>",
"mask_token": "<mask>"
} |