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README.md

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+---
+tags:
+- uv-script
+- text-classification
+- hf-jobs
+base_model: LiquidAI/LFM2.5-Encoder-350M
+datasets:
+- davanstrien/dataset-rows-with-task-categories
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# dataset-rows-task-classifier-2048
+
+[LiquidAI/LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) fine-tuned for
+multi-label text classification on
+[davanstrien/dataset-rows-with-task-categories](https://huggingface.co/datasets/davanstrien/dataset-rows-with-task-categories).
+
+- **Labels (35)**: `audio-classification`, `audio-to-audio`, `automatic-speech-recognition`, `feature-extraction`, `fill-mask`, `image-classification`, `image-feature-extraction`, `image-segmentation`, `image-text-to-text`, `image-to-3d`, `image-to-image`, `image-to-text`, `multiple-choice`, `object-detection`, `question-answering`, `reinforcement-learning`, `robotics`, `sentence-similarity`, `summarization`, `table-question-answering`, `tabular-classification`, `tabular-regression`, `text-classification`, `text-generation`, `text-retrieval`, `text-to-image`, `text-to-speech`, `text-to-video`, `time-series-forecasting`, `token-classification`, … (35 total)
+- **Date**: 2026-07-29 18:35 UTC
+
+> [!NOTE]
+> This 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.
+
+## Evaluation
+
+| Metric | Value |
+|--------|-------|
+| f1_micro @ 0.5 | 0.6218 |
+| f1_macro @ 0.5 | 0.4621 |
+| f1_micro @ tuned | 0.6486 |
+| f1_macro @ tuned | 0.5594 |
+
+Per-label decision thresholds tuned on the eval split are stored in
+`config.classifier_thresholds`.
+
+**Choosing an operating point**: the stored thresholds maximise per-label F1. For
+precision-first use (e.g. auto-applying labels), act only on predictions well above
+their threshold — sigmoid probabilities are a usable confidence signal, and filtering
+to high-confidence predictions trades coverage for precision. Route the rest to review.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("davanstrien/dataset-rows-task-classifier-2048", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("davanstrien/dataset-rows-task-classifier-2048", trust_remote_code=True)
+
+inputs = tokenizer("your text here", return_tensors="pt", truncation=True)
+probs = torch.sigmoid(model(**inputs).logits)[0]
+thresholds = torch.tensor(model.config.classifier_thresholds)  # tuned on validation
+labels = [model.config.id2label[i] for i in (probs >= thresholds).nonzero().flatten().tolist()]
+print(labels)
+```
+
+## Reproduction
+
+Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs) (`gpu`) with the [`train-classifier.py`](https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py) recipe from [uv-scripts](https://huggingface.co/uv-scripts). Run it yourself:
+
+```bash
+hf jobs uv run --flavor gpu --secrets HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \
+    davanstrien/dataset-rows-with-task-categories davanstrien/dataset-rows-task-classifier-2048 --label-column labels
+```