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)# 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
- Xet hash:
- 51a076b622e90f61bc9817239aacbaeabc71d94dec8dbc258817d2d0a50d56ce
- Size of remote file:
- 1.42 GB
- SHA256:
- 628d648c0754a5a50838f9251491fa4a116a59fb11d035849e7b00e4ab51c725
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