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
File size: 119 Bytes
4e2f724 | 1 2 3 4 5 6 7 | {
"encoder_name": "LiquidAI/LFM2.5-Encoder-350M",
"hidden_size": 1024,
"mode": "cross",
"type_feature": true
}
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