Token Classification
Transformers
Safetensors
lfm2
feature-extraction
liquid
lfm2.5
bidirectional
masked-lm
encoder
custom_code
Instructions to use LiquidAI/LFM2.5-Encoder-350M-Policy-Linter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-Encoder-350M-Policy-Linter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", trust_remote_code=True) model = AutoModel.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be83cf3088431f7047fab1586eb35059f09cbb5b133f0ffc0fdc03cf5af627f7
- Size of remote file:
- 1.42 GB
- SHA256:
- 96c70cb285aa8515361b695cd18330a381a52122ebe91e1be9f224b6537ab4a9
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