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
Download onnx/spendcoder.json from Saeedhoj/spendcoder-distilroberta: direct link, hf CLI and curl.
- Browser
- Download file 874 Bytes
-
https://huggingface.co/Saeedhoj/spendcoder-distilroberta/resolve/main/onnx/spendcoder.json
- Command line
-
hf download hf://Saeedhoj/spendcoder-distilroberta/onnx/spendcoder.json
-
curl -L -o spendcoder.json https://huggingface.co/Saeedhoj/spendcoder-distilroberta/resolve/main/onnx/spendcoder.json
874 Bytes
| { | |
| "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 | |
| } | |
| } | |
| } |