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: 470 Bytes
4e2f724 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | [
{
"source": "She go to school every day .",
"corrected": "She goes to school every day ."
},
{
"source": "I has went to the stor yesterday .",
"corrected": "I went to the store yesterday ."
},
{
"source": "Their are many reason to study hard .",
"corrected": "There are many reasons to study hard ."
},
{
"source": "He don't like coffee but he like tea .",
"corrected": "He does n't like coffee , but he likes tea ."
}
] |