fancyzhx/ag_news
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How to use davanstrien/lfm25-encoder-agnews-smoke with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="davanstrien/lfm25-encoder-agnews-smoke", trust_remote_code=True) # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("davanstrien/lfm25-encoder-agnews-smoke", trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("davanstrien/lfm25-encoder-agnews-smoke", trust_remote_code=True, device_map="auto")LiquidAI/LFM2.5-Encoder-350M fine-tuned for single-label text classification on fancyzhx/ag_news.
World, Sports, Business, Sci/TechThis model uses a custom classification head (mean pooling over a backbone without a native sequence-classification class), so loading requires
trust_remote_code=True. vLLM serving requires a standard architecture.
| Metric | Value |
|---|---|
| accuracy | 0.7575 |
| f1_macro | 0.7568 |
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("davanstrien/lfm25-encoder-agnews-smoke", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("davanstrien/lfm25-encoder-agnews-smoke", trust_remote_code=True)
inputs = tokenizer("your text here", return_tensors="pt", truncation=True)
print(model.config.id2label[model(**inputs).logits.argmax().item()])
Produced on Hugging Face Jobs (gpu) with the train-classifier.py recipe from uv-scripts. Run it yourself:
hf jobs uv run --flavor gpu --secrets HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \
fancyzhx/ag_news davanstrien/lfm25-encoder-agnews-smoke
Base model
LiquidAI/LFM2.5-350M-Base