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-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devseis/devseis-ai-act-classifier-v6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Devseis/devseis-ai-act-classifier-v6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Devseis/devseis-ai-act-classifier-v6") model = AutoModelForSequenceClassification.from_pretrained("Devseis/devseis-ai-act-classifier-v6", device_map="auto") - Transformers.js
How to use Devseis/devseis-ai-act-classifier-v6 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-v6'); - Notebooks
- Google Colab
- Kaggle
Claude Opus 5.5
Devseis AI Act Classifier v6: weights, int8 ONNX, card and evaluation
7c594f6 verified Download evaluation/export_check.json from Devseis/devseis-ai-act-classifier-v6: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/Devseis/devseis-ai-act-classifier-v6/resolve/main/evaluation/export_check.json
- Command line
-
hf download hf://Devseis/devseis-ai-act-classifier-v6/evaluation/export_check.json
-
curl -L -o export_check.json https://huggingface.co/Devseis/devseis-ai-act-classifier-v6/resolve/main/evaluation/export_check.json
2.04 kB
| { | |
| "source": "results_v6/models/legalbert", | |
| "quantisation": { | |
| "weight_type": "QUInt8", | |
| "per_channel": false, | |
| "reduce_range": false | |
| }, | |
| "variants_tried": [ | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": true, | |
| "reduce_range": false, | |
| "val_agreement": 0.9506, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "val_agreement": 0.9753, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": false, | |
| "reduce_range": false, | |
| "val_agreement": 0.963, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QInt8", | |
| "per_channel": false, | |
| "reduce_range": true, | |
| "val_agreement": 0.9506, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": true, | |
| "reduce_range": false, | |
| "val_agreement": 0.9753, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "val_agreement": 0.9691, | |
| "mb": 35.6 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": false, | |
| "reduce_range": false, | |
| "val_agreement": 0.9753, | |
| "mb": 35.5 | |
| }, | |
| { | |
| "weight_type": "QUInt8", | |
| "per_channel": false, | |
| "reduce_range": true, | |
| "val_agreement": 0.9568, | |
| "mb": 35.5 | |
| } | |
| ], | |
| "int8_mb": 35.5, | |
| "fp32_mb": 140.4, | |
| "agreement": 0.9905, | |
| "n_rows": 210, | |
| "disagreements": [ | |
| { | |
| "text": "Proposed use case: a financial newsletter auto-generating market commentary articles published directly to subscribers.", | |
| "true": "limited_risk", | |
| "pytorch": "limited_risk", | |
| "onnx_int8": "minimal_risk" | |
| }, | |
| { | |
| "text": "Our client wants to deploy a language-learning app's AI that decides a learner's official proficiency level, steering their curriculum path.", | |
| "true": "high_risk", | |
| "pytorch": "minimal_risk", | |
| "onnx_int8": "high_risk" | |
| } | |
| ], | |
| "test_accuracy_pytorch": 0.8333, | |
| "test_accuracy_onnx_int8": 0.8333 | |
| } |