Text Classification
Transformers
ONNX
Safetensors
Persian
English
xlm-roberta
feature-extraction
persian
farsi
decision-model
tool-calling
agent
calibration
multiple-choice
cross-encoder
reranker
open-weight
dibachain
dibaone
text-embeddings-inference
Instructions to use Dibachain/DibaOne-X1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dibachain/DibaOne-X1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dibachain/DibaOne-X1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Dibachain/DibaOne-X1") model = AutoModel.from_pretrained("Dibachain/DibaOne-X1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download calibration.json from Dibachain/DibaOne-X1: direct link, hf CLI and curl.
- Browser
- Download file 377 Bytes
-
https://huggingface.co/Dibachain/DibaOne-X1/resolve/main/calibration.json
- Command line
-
hf download hf://Dibachain/DibaOne-X1/calibration.json
-
curl -L -o calibration.json https://huggingface.co/Dibachain/DibaOne-X1/resolve/main/calibration.json
377 Bytes
| { | |
| "temperature": { | |
| "devpath|0-4": 4.65743, | |
| "devpath|11-24": 4.75112, | |
| "devpath|25-64": 6.36288, | |
| "devpath|5-10": 5.72168, | |
| "toolcall|0-4": 1.87568, | |
| "toolcall|11-24": 3.4091, | |
| "wikirace|11-24": 1.30193 | |
| }, | |
| "default_temperature": 2.15627, | |
| "abstain": { | |
| "devpath": 0.078303, | |
| "toolcall": 0.10049, | |
| "wikirace": 0.0 | |
| }, | |
| "default_abstain": 0.0, | |
| "fitted_on": "dev" | |
| } |