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
Download tokenizer_config.json from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 370 Bytes
-
https://huggingface.co/Angshul/SpliNet/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Angshul/SpliNet/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Angshul/SpliNet/resolve/main/tokenizer_config.json
370 Bytes
| { | |
| "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>" | |
| } |