Ashybalka/xlm-roberta-taxonomy-main-de

Ashybalka/xlm-roberta-taxonomy-main-de/
├── config.json                    # shared — label map, model config
├── tokenizer.json                 # shared — fast tokenizer
├── tokenizer_config.json          # shared
├── sentencepiece.bpe.model        # shared
├── special_tokens_map.json        # shared
├── test_metrics.json              # evaluation results
├── classification_report.txt      # full per-class report
│
├── pytorch/
│   ├── config.json                # needed for from_pretrained(subfolder=)
│   └── model.safetensors          # GPU inference / fine-tuning (~1.1 GB)
│
├── onnx/
│   └── model.onnx                 # CPU fp32 inference (~1.1 GB)
│
└── onnx-int8/
    └── model_quantized.onnx       # CPU INT8 quantized (~280 MB 3-4× smaller)

[Job Title] [SEP] [Job Description]

text = "Pflegefachkraft [SEP] Wir suchen eine examinierte Pflegefachkraft " \
       "für die Betreuung von Bewohnern in unserer Senioreneinrichtung."

from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="Ashybalka/xlm-roberta-taxonomy-main-de",
    subfolder="pytorch",
    device=0,  # GPU; -1 for CPU
)

result = clf("DevOps Engineer [SEP] Kubernetes, CI/CD, Monitoring mit Prometheus.")
print(result)
# [{'label': 'IT & Software', 'score': 0.9712}]

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

model     = ORTModelForSequenceClassification.from_pretrained(
                "Ashybalka/xlm-roberta-taxonomy-main-de",
                subfolder="onnx"
            )
tokenizer = AutoTokenizer.from_pretrained("Ashybalka/xlm-roberta-taxonomy-main-de")
clf       = pipeline("text-classification", model=model, tokenizer=tokenizer)

result = clf("Maurer [SEP] Erfahrener Maurer für Hochbau und Sanierungsarbeiten gesucht.")
print(result)
# [{'label': 'Construction & Building', 'score': 0.9483}]

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

model     = ORTModelForSequenceClassification.from_pretrained(
                "Ashybalka/xlm-roberta-taxonomy-main-de",
                subfolder="onnx-int8"
            )
tokenizer = AutoTokenizer.from_pretrained("Ashybalka/xlm-roberta-taxonomy-main-de")
clf       = pipeline("text-classification", model=model, tokenizer=tokenizer)

import json
import numpy as np
import onnxruntime as ort
from huggingface_hub import snapshot_download
from tokenizers import Tokenizer

# download model
path = snapshot_download(
    "Ashybalka/xlm-roberta-taxonomy-main-de",
    allow_patterns=["onnx/model.onnx", "tokenizer.json", "config.json"]
)

# load
session   = ort.InferenceSession(f"{path}/onnx/model.onnx",
                                  providers=["CPUExecutionProvider"])
tokenizer = Tokenizer.from_file(f"{path}/tokenizer.json")
tokenizer.enable_truncation(max_length=510)
tokenizer.no_padding()

with open(f"{path}/config.json") as f:
    labels = [v for _, v in sorted(json.load(f)["id2label"].items(), key=lambda x: int(x[0]))]

vocab    = tokenizer.get_vocab()
bos, eos = vocab["<s>"], vocab["</s>"]

def classify(title: str, description: str) -> dict:
    text     = f"{title} [SEP] {description}"
    encoding = tokenizer.encode(text, add_special_tokens=False)
    ids      = [bos] + encoding.ids + [eos]
    mask     = [1] * len(ids)

    logits = session.run(None, {
        "input_ids":      np.array([ids],  dtype=np.int64),
        "attention_mask": np.array([mask], dtype=np.int64),
    })[0][0]

    exp   = np.exp(logits - logits.max())
    probs = exp / exp.sum()
    idx   = int(np.argmax(probs))

    return {"category": labels[idx], "confidence": round(float(probs[idx]), 4)}

print(classify("Python Developer", "FastAPI, Docker, PostgreSQL, REST API Entwicklung."))
# {'category': 'IT & Software', 'confidence': 0.9534}