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
File size: 2,757 Bytes
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"model_type": "clf_frozen",
"base_model": "distilbert/distilbert-base-uncased",
"pinned_revision": "12040accade4e8a0f71eabdb258fecc2e7e948be",
"label_space_size": 65,
"label_space": [
"air_quality_pm25_analyzer",
"ar_marker_bundle_adjuster",
"audio_downsampling_resampler",
"bgp_route_leak_detector",
"bridge_overheight_vehicle_detector",
"camera_tamper_detector",
"car_alarm_detector",
"chat_message_translator",
"container_stack_counter",
"coronary_calcium_ct_triage",
"count",
"crowd_surge_detector",
"customs_declaration_parser",
"dense_depth_map_completer",
"detection",
"drone_perimeter_alert",
"emergency_siren_detector",
"facial_blendshape_animator",
"fence_climbing_detector",
"fitting_room_occupancy_sensor",
"fundus_diabetic_retinopathy_triage",
"gaussian_splat_renderer",
"geotiff_raster_compressor",
"h264_bitrate_downscaler",
"handwritten_form_ocr",
"industrial_bearing_monitor",
"insurance_claim_form_parser",
"ip_fragmentation_attack_detector",
"ja_to_en_menu_translator",
"json_to_protobuf_serializer",
"label_alignment_inspector",
"loss_prevention_sweethearting_detector",
"medical_prescription_parser",
"ocr",
"packet_jitter_analyzer",
"parking_spot_counter",
"pcb_solder_bridge_inspector",
"pothole_road_surface_scanner",
"prescription_label_ocr",
"produce_ripeness_scanner",
"product_unit_counter",
"puddle_detector",
"radiation_geiger_counter_monitor",
"roundabout_gap_analyzer",
"rubber_o_ring_flaw_inspector",
"runway_incursion_detector",
"secure_server_rack_door_monitor",
"self_checkout_item_mismatch_detector",
"shipping_label_ocr",
"shipping_manifest_digitizer",
"skin_lesion_melanoma_triage",
"solar_cell_microcrack_inspector",
"tcp_retransmission_monitor",
"technical_terminology_normalizer",
"unattended_baggage_detector",
"uv_index_solar_radiometer",
"visitor_id_scanner",
"wake_word_spotter",
"weapon_detector",
"wildfire_thermal_hotspot_detector",
"work_zone_merge_compliance_monitor",
"wrist_scaphoid_fracture_triage",
"wrong_way_driver_detector",
"zh_to_en_subtitle_generator",
"unsupported"
],
"data_counts": {
"model_type": "clf_frozen",
"label_space_size": 65,
"synthetic_descriptions": 64,
"dev_cases": 258,
"adaptation_examples": 0,
"total_examples": 322
},
"adaptation_examples_count": 0,
"epochs": 10,
"batch_size": 16,
"learning_rate": 5e-05,
"weight_decay": 0.01,
"max_len": 128,
"seed": 20260924,
"device": "mps",
"wall_time_s": 12.100141048431396,
"final_train_loss": 0.7863900039506995
} |