Text Classification
Transformers
ONNX
Safetensors
English
roberta
editlens
ai-detection
quantization
local-inference
text-embeddings-inference
Instructions to use CoderBak/editlens_roberta_modelkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CoderBak/editlens_roberta_modelkit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CoderBak/editlens_roberta_modelkit")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CoderBak/editlens_roberta_modelkit") model = AutoModelForSequenceClassification.from_pretrained("CoderBak/editlens_roberta_modelkit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download validation/fp32.json from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 4.7 kB
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/validation/fp32.json
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/validation/fp32.json
-
curl -L -o fp32.json https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/validation/fp32.json
4.7 kB
| { | |
| "variant": "fp32", | |
| "model": "onnx/model.onnx", | |
| "model_sha256": "ddd1173f2ef517ad499965e5029fae8099a8054a2bc76d8134e5889cc4ed1b3e", | |
| "reference_npz_sha256": "401ca4e9bf9b65c3f91552b277f254254fa1469cf870ac81424985df3d92286c", | |
| "fixtures_sha256": "b4cfec82a9c2839e3231a51a4fe991c5ce83ae516aa61e8fe5f93c235953651b", | |
| "provider": "CPUExecutionProvider", | |
| "onnxruntime": "1.30.0", | |
| "platform": { | |
| "os": "Darwin", | |
| "version": "27.2", | |
| "machine": "arm64" | |
| }, | |
| "threads": 4, | |
| "load_seconds": 1.27055700001074, | |
| "cases": [ | |
| { | |
| "id": "empty", | |
| "shape": [ | |
| 1, | |
| 2 | |
| ], | |
| "max_absolute_logit_difference": 3.337860107421875e-06, | |
| "max_absolute_probability_difference": 5.654622786677166e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.036468790989601985 | |
| }, | |
| { | |
| "id": "minimal", | |
| "shape": [ | |
| 1, | |
| 4 | |
| ], | |
| "max_absolute_logit_difference": 5.7220458984375e-06, | |
| "max_absolute_probability_difference": 5.157515448050987e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.01998937499593012 | |
| }, | |
| { | |
| "id": "short", | |
| "shape": [ | |
| 1, | |
| 33 | |
| ], | |
| "max_absolute_logit_difference": 2.1457672119140625e-06, | |
| "max_absolute_probability_difference": 1.651584634609904e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.04194808300235309 | |
| }, | |
| { | |
| "id": "mixed_padding", | |
| "shape": [ | |
| 4, | |
| 42 | |
| ], | |
| "max_absolute_logit_difference": 1.3828277587890625e-05, | |
| "max_absolute_probability_difference": 2.085083897984674e-06, | |
| "argmax_agreements": 4, | |
| "samples": 4, | |
| "single_run_seconds": 0.1256193750014063 | |
| }, | |
| { | |
| "id": "technical", | |
| "shape": [ | |
| 4, | |
| 75 | |
| ], | |
| "max_absolute_logit_difference": 1.7523765563964844e-05, | |
| "max_absolute_probability_difference": 4.016848888621638e-06, | |
| "argmax_agreements": 4, | |
| "samples": 4, | |
| "single_run_seconds": 0.20917212500353344 | |
| }, | |
| { | |
| "id": "formatting", | |
| "shape": [ | |
| 4, | |
| 93 | |
| ], | |
| "max_absolute_logit_difference": 5.841255187988281e-06, | |
| "max_absolute_probability_difference": 1.8292948700815437e-06, | |
| "argmax_agreements": 4, | |
| "samples": 4, | |
| "single_run_seconds": 0.2499902910203673 | |
| }, | |
| { | |
| "id": "repetition", | |
| "shape": [ | |
| 2, | |
| 512 | |
| ], | |
| "max_absolute_logit_difference": 8.344650268554688e-06, | |
| "max_absolute_probability_difference": 9.791367671097007e-07, | |
| "argmax_agreements": 2, | |
| "samples": 2, | |
| "single_run_seconds": 0.780980624986114 | |
| }, | |
| { | |
| "id": "long_512", | |
| "shape": [ | |
| 1, | |
| 512 | |
| ], | |
| "max_absolute_logit_difference": 3.933906555175781e-06, | |
| "max_absolute_probability_difference": 1.4080306614400184e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.3768378750246484 | |
| }, | |
| { | |
| "id": "long_mixed_batch", | |
| "shape": [ | |
| 2, | |
| 512 | |
| ], | |
| "max_absolute_logit_difference": 2.384185791015625e-06, | |
| "max_absolute_probability_difference": 5.780850338421573e-07, | |
| "argmax_agreements": 2, | |
| "samples": 2, | |
| "single_run_seconds": 0.7969407500058878 | |
| }, | |
| { | |
| "id": "whitespace", | |
| "shape": [ | |
| 2, | |
| 17 | |
| ], | |
| "max_absolute_logit_difference": 2.0265579223632812e-05, | |
| "max_absolute_probability_difference": 1.538472105155897e-06, | |
| "argmax_agreements": 2, | |
| "samples": 2, | |
| "single_run_seconds": 0.03817816698574461 | |
| }, | |
| { | |
| "id": "sequence_128", | |
| "shape": [ | |
| 1, | |
| 129 | |
| ], | |
| "max_absolute_logit_difference": 2.2649765014648438e-06, | |
| "max_absolute_probability_difference": 5.777336291590274e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.09674633399117738 | |
| }, | |
| { | |
| "id": "sequence_256", | |
| "shape": [ | |
| 1, | |
| 257 | |
| ], | |
| "max_absolute_logit_difference": 2.6226043701171875e-06, | |
| "max_absolute_probability_difference": 8.311704222663252e-07, | |
| "argmax_agreements": 1, | |
| "samples": 1, | |
| "single_run_seconds": 0.18392837498686276 | |
| } | |
| ], | |
| "max_absolute_probability_difference": 4.016848888621638e-06, | |
| "argmax_disagreements": 0, | |
| "samples": 24, | |
| "acceptance_probability_tolerance": 0.0001, | |
| "passed": true, | |
| "limitations": "Synthetic unlabeled conversion fixtures. Timing is one run per shape, not a comparative performance benchmark. No Windows, Linux, CUDA, DirectML, WinML or CoreML validation is implied." | |
| } | |