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 tokenizer_config.json from Dibachain/DibaOne-X1: direct link, hf CLI and curl.
- Browser
- Download file 353 Bytes
-
https://huggingface.co/Dibachain/DibaOne-X1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Dibachain/DibaOne-X1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Dibachain/DibaOne-X1/resolve/main/tokenizer_config.json
353 Bytes
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } |