Instructions to use boapps/kmdb_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boapps/kmdb_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boapps/kmdb_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boapps/kmdb_classification_model") model = AutoModelForSequenceClassification.from_pretrained("boapps/kmdb_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,140 Bytes
bb912d1 63bfdac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | Klasszifikációs modell: a [kmdb_classification](https://huggingface.co/datasets/boapps/kmdb_classification) adathalmazon lett finomhangolva a huBERT modell. A klasszifikáció cím és leírás (lead) alapján történik.
### Használat:
```python
import torch
import torch.nn.functional as F
from transformers import BertForSequenceClassification, BertTokenizer
from datasets import load_dataset
model = BertForSequenceClassification.from_pretrained('boapps/kmdb_classification_model')
tokenizer = BertTokenizer.from_pretrained('SZTAKI-HLT/hubert-base-cc')
article = {'title': '400 milliós luxusvillába vette be magát Matolcsy és családja', 'description': 'Matolcsy György fiának cége megvette, Matolcsy György unokatestvérének bankja meghitelezte, Matolcsy György pedig használja a 430 millióért hirdetett II. kerületi luxusrezidenciát.'}
tokenized_article = tokenizer(article['title']+'\n'+article['description'], return_tensors="pt")
logits = model(**tokenized_article).logits
probabilities = F.softmax(logits[0], dim=-1)
print(probabilities)
```
### Eredmények
precision: 0.739
recall: 0.950
accuracy: 0.963 |