Token Classification
Transformers
Safetensors
gec_tagger
feature-extraction
liquid
lfm2
lfm2.5
bidirectional
masked-lm
encoder
grammatical-error-correction
gec
spell-check
gector
custom_code
Instructions to use LiquidAI/LFM2.5-Encoder-350M-Spellchecker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-Encoder-350M-Spellchecker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LiquidAI/LFM2.5-Encoder-350M-Spellchecker", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-Spellchecker", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from LiquidAI/LFM2.5-Encoder-350M-Spellchecker: direct link, hf CLI and curl.
- Browser
- Download file 306 Bytes
-
https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://LiquidAI/LFM2.5-Encoder-350M-Spellchecker/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker/resolve/main/tokenizer_config.json
306 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "is_local": false, | |
| "mask_token": "<|mask|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|pad|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |