YAML Metadata Warning:The pipeline tag "intent-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Intent Classifier Experiments
This Hugging Face repo stores all experiment-time adapter artifacts for the intent-classifier project.
Purpose
- Keep every adapter checkpoint produced during fine-tuning.
- Preserve per-experiment traceability across versions.
- Keep release artifacts separate from experimental artifacts.
Layout
- v1.0/
- one folder per experiment run
- naming format:
- {model}{technique}{config}{dataset_size}{YYYYMMDD-HHMMSS}
Each experiment folder can include:
- adapter weights
- tokenizer/config files
- metadata and training outputs
Related repositories
Training code and release models are maintained separately.
- Training code: https://github.com/kon172verma/intent-classifier
- Inference/benchmarking: https://github.com/kon172verma/intent-classifier-inference
- Release: https://huggingface.co/kon172verma/intent-classifier
Versioning
Experiment folders are grouped by release version (for example, v1.0). When a version is finalized, only selected best models are promoted to the release repo.
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