Text Classification
Transformers
Safetensors
English
edge-computing
service-orchestration
intent-classification
Instructions to use UTSCybeR/Edge-Computing-JEV-classifiers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UTSCybeR/Edge-Computing-JEV-classifiers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UTSCybeR/Edge-Computing-JEV-classifiers")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UTSCybeR/Edge-Computing-JEV-classifiers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download clf_retrained_25/training.json from UTSCybeR/Edge-Computing-JEV-classifiers: direct link, hf CLI and curl.
- Browser
- Download file 2.84 kB
-
https://huggingface.co/UTSCybeR/Edge-Computing-JEV-classifiers/resolve/main/clf_retrained_25/training.json
- Command line
-
hf download hf://UTSCybeR/Edge-Computing-JEV-classifiers/clf_retrained_25/training.json
-
curl -L -o training.json https://huggingface.co/UTSCybeR/Edge-Computing-JEV-classifiers/resolve/main/clf_retrained_25/training.json
2.84 kB
| { | |
| "model_type": "clf_retrained_25", | |
| "base_model": "distilbert/distilbert-base-uncased", | |
| "pinned_revision": "12040accade4e8a0f71eabdb258fecc2e7e948be", | |
| "label_space_size": 65, | |
| "label_space": [ | |
| "ambient_weather_station_aggregator", | |
| "ar_marker_bundle_adjuster", | |
| "audio_downsampling_resampler", | |
| "audio_event_classifier", | |
| "bgp_route_leak_detector", | |
| "bridge_overheight_vehicle_detector", | |
| "camera_tamper_detector", | |
| "chat_message_translator", | |
| "container_stack_counter", | |
| "conveyor_belt_tear_inspector", | |
| "coronary_calcium_ct_triage", | |
| "count", | |
| "crane_hook_detector", | |
| "crowd_surge_detector", | |
| "customs_declaration_parser", | |
| "dense_depth_map_completer", | |
| "drone_perimeter_alert", | |
| "facial_blendshape_animator", | |
| "fence_climbing_detector", | |
| "fitting_room_occupancy_sensor", | |
| "flood_water_level_gauge", | |
| "fundus_diabetic_retinopathy_triage", | |
| "gaussian_splat_renderer", | |
| "geotiff_raster_compressor", | |
| "glass_break_detector", | |
| "glass_vial_crack_inspector", | |
| "h264_bitrate_downscaler", | |
| "handwritten_form_ocr", | |
| "hazard_cone_detector", | |
| "insurance_claim_form_parser", | |
| "ip_fragmentation_attack_detector", | |
| "ja_to_en_menu_translator", | |
| "json_to_protobuf_serializer", | |
| "loss_prevention_sweethearting_detector", | |
| "medical_prescription_parser", | |
| "ocr", | |
| "packet_jitter_analyzer", | |
| "parking_spot_counter", | |
| "pedestrian_detector", | |
| "pothole_road_surface_scanner", | |
| "prescription_label_ocr", | |
| "produce_ripeness_scanner", | |
| "product_unit_counter", | |
| "refrigeration_temperature_compliance_logger", | |
| "roundabout_gap_analyzer", | |
| "runway_incursion_detector", | |
| "secure_server_rack_door_monitor", | |
| "self_checkout_item_mismatch_detector", | |
| "shipping_label_ocr", | |
| "shipping_manifest_digitizer", | |
| "skin_lesion_melanoma_triage", | |
| "speech_to_text", | |
| "tcp_retransmission_monitor", | |
| "technical_terminology_normalizer", | |
| "turbine_blade_thermal_inspector", | |
| "vehicle_detector", | |
| "visitor_id_scanner", | |
| "water_leak_acoustic_detector", | |
| "water_turbidity_clarity_meter", | |
| "weld_seam_flaw_inspector", | |
| "work_zone_merge_compliance_monitor", | |
| "wrist_scaphoid_fracture_triage", | |
| "wrong_way_driver_detector", | |
| "zh_to_en_subtitle_generator", | |
| "unsupported" | |
| ], | |
| "data_counts": { | |
| "model_type": "clf_retrained_25", | |
| "label_space_size": 65, | |
| "synthetic_descriptions": 0, | |
| "dev_cases": 0, | |
| "adaptation_examples": 76, | |
| "new_descriptions": 16, | |
| "churn_dev_cases": 60, | |
| "total_examples": 339 | |
| }, | |
| "adaptation_examples_count": 76, | |
| "epochs": 10, | |
| "batch_size": 16, | |
| "learning_rate": 5e-05, | |
| "weight_decay": 0.01, | |
| "max_len": 128, | |
| "seed": 20260924, | |
| "device": "mps", | |
| "wall_time_s": 12.815657138824463, | |
| "final_train_loss": 0.6284485658009847 | |
| } |