Text Classification
Transformers
ONNX
Safetensors
Transformers.js
bert
eu-ai-act
ai-governance
legal
devseis
research-note
text-embeddings-inference
Instructions to use Devseis/devseis-ai-act-classifier-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devseis/devseis-ai-act-classifier-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Devseis/devseis-ai-act-classifier-v5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Devseis/devseis-ai-act-classifier-v5") model = AutoModelForSequenceClassification.from_pretrained("Devseis/devseis-ai-act-classifier-v5", device_map="auto") - Transformers.js
How to use Devseis/devseis-ai-act-classifier-v5 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'Devseis/devseis-ai-act-classifier-v5'); - Notebooks
- Google Colab
- Kaggle
Claude Opus 5.5
Devseis AI Act Classifier v5: weights, int8 ONNX, card and evaluation
515d915 verified Download evaluation/export_check.json from Devseis/devseis-ai-act-classifier-v5: direct link, hf CLI and curl.
- Browser
- Download file 2.26 kB
-
https://huggingface.co/Devseis/devseis-ai-act-classifier-v5/resolve/main/evaluation/export_check.json
- Command line
-
hf download hf://Devseis/devseis-ai-act-classifier-v5/evaluation/export_check.json
-
curl -L -o export_check.json https://huggingface.co/Devseis/devseis-ai-act-classifier-v5/resolve/main/evaluation/export_check.json
2.26 kB
| { | |
| "source": "results_v5/models/legalbert", | |
| "quantisation": { | |
| "weight_type": "QInt8", | |
| "per_channel": true, | |
| "reduce_range": false | |
| }, | |
| "variants_tried": [ | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": true, | |
| "reduce_range": false, | |
| "val_agreement": 0.9796, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "val_agreement": 0.9592, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": false, | |
| "reduce_range": false, | |
| "val_agreement": 0.9728, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": false, | |
| "reduce_range": true, | |
| "val_agreement": 0.9252, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": true, | |
| "reduce_range": false, | |
| "val_agreement": 0.9592, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "val_agreement": 0.9388, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": false, | |
| "reduce_range": false, | |
| "val_agreement": 0.9456, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": false, | |
| "reduce_range": true, | |
| "val_agreement": 0.9456, | |
| "mb": 35.5 | |
| } | |
| ], | |
| "int8_mb": 35.6, | |
| "fp32_mb": 140.4, | |
| "agreement": 0.9848, | |
| "n_rows": 198, | |
| "disagreements": [ | |
| { | |
| "text": "Our client wants to deploy a political chatbot conversing with voters to persuade them toward a specific candidate.", | |
| "true": "high_risk", | |
| "pytorch": "limited_risk", | |
| "onnx_int8": "prohibited" | |
| }, | |
| { | |
| "text": "A startup is building a stadium's live biometric camera system identifying any attendee in real time for general crowd management.", | |
| "true": "high_risk", | |
| "pytorch": "minimal_risk", | |
| "onnx_int8": "high_risk" | |
| }, | |
| { | |
| "text": "We are developing software analysing a city council's call-volume data to plan how many staff to put on the phones.", | |
| "true": "minimal_risk", | |
| "pytorch": "minimal_risk", | |
| "onnx_int8": "high_risk" | |
| } | |
| ], | |
| "test_accuracy_pytorch": 0.7929, | |
| "test_accuracy_onnx_int8": 0.7929 | |
| } |