Feature Extraction
Transformers
Safetensors
English
matilda_jev
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/matilda-jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/matilda-jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenization_matilda_jev.py from Maincode/matilda-jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 280 Bytes
-
https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/tokenization_matilda_jev.py
- Command line
-
hf download hf://Maincode/matilda-jev-v1/tokenization_matilda_jev.py
-
curl -L -o tokenization_matilda_jev.py https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/tokenization_matilda_jev.py
280 Bytes
| """MATILDA tokenizer alias: vocabulary, normalization and special tokens are unchanged.""" | |
| from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer | |
| class MatildaJevTokenizer(Qwen2Tokenizer): | |
| pass | |
| MatildaJevTokenizer.register_for_auto_class("AutoTokenizer") | |