Text Classification
Transformers
ONNX
Safetensors
English
roberta
finance
accounting
spend-classification
procurement
text-embeddings-inference
Instructions to use Saeedhoj/spendcoder-distilroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saeedhoj/spendcoder-distilroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Saeedhoj/spendcoder-distilroberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Saeedhoj/spendcoder-distilroberta") model = AutoModelForSequenceClassification.from_pretrained("Saeedhoj/spendcoder-distilroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 874 Bytes
5f872c2 f5a3393 5f872c2 f5a3393 5f872c2 f5a3393 5f872c2 | 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 28 29 30 31 32 33 34 | {
"labels": [
"Engineering & professional services",
"Research & development",
"Facility operations & management",
"Construction & building maintenance",
"Equipment maintenance & repair",
"IT & telecom services",
"Environmental services",
"Utilities, security & housekeeping",
"Other services",
"IT hardware & software",
"Electrical & power equipment",
"Lab instruments & chemicals",
"Vehicles & machinery",
"Other goods"
],
"max_length": 96,
"pad_id": 1,
"pad_token": "<pad>",
"threshold": 0.99,
"metrics": {
"baseline_test": {
"accuracy": 0.6729153199741436,
"macro_f1": 0.616396115797141,
"weighted_f1": 0.6746384382104165
},
"finetuned_test": {
"accuracy": 0.665158371040724,
"macro_f1": 0.5694653060821443,
"weighted_f1": 0.663787588778561
}
}
} |