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: 494 Bytes
4e2f724 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"architectures": [
"GecTaggerForGEC"
],
"model_type": "gec_tagger",
"auto_map": {
"AutoConfig": "modeling_gectagger.GecTaggerConfig",
"AutoModel": "modeling_gectagger.GecTaggerForGEC"
},
"encoder_name": "LiquidAI/LFM2.5-Encoder-350M",
"num_tags": 128802,
"hidden_size": 1024,
"tie_replace": true,
"multi_head": false,
"aux_loss_weight": 0.5,
"use_swap": false,
"qat_applied": false,
"qat_group_size": 32,
"dropout": 0.1,
"torch_dtype": "float16"
}
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