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
| { | |
| "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" | |
| } | |