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_metadata.json from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 489 Bytes
-
https://huggingface.co/Angshul/SpliNet/resolve/main/tokenizer_metadata.json
- Command line
-
hf download hf://Angshul/SpliNet/tokenizer_metadata.json
-
curl -L -o tokenizer_metadata.json https://huggingface.co/Angshul/SpliNet/resolve/main/tokenizer_metadata.json
489 Bytes
| { | |
| "source": "allenai/c4", | |
| "config": "en", | |
| "split": "train", | |
| "model_type": "unigram", | |
| "vocab_size": 32000, | |
| "lowercase": true, | |
| "normalization": "NFKC + SentencePiece nmt_nfkc", | |
| "training_text_bytes": 536874807, | |
| "training_documents": 247312, | |
| "special_token_ids": { | |
| "<unk>": 0, | |
| "<s>": 1, | |
| "</s>": 2, | |
| "<pad>": 3, | |
| "<cls>": 4, | |
| "<sep>": 5, | |
| "<mask>": 6 | |
| }, | |
| "sha256_spiece_model": "119ec6b2af9cbbc56f297bd606b69f79f6e3130a34ee56122ee813a58d15bb9d" | |
| } |