Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
albert
pretraining
multilingual
masked-language-modeling
sentence-order-prediction
xlmindic
nlp
indoaryan
indicnlp
iso15919
Instructions to use ibraheemmoosa/xlmindic-base-multiscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-multiscript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ibraheemmoosa/xlmindic-base-multiscript")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript") model = AutoModelForPreTraining.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ibraheemmoosa/xlmindic-base-multiscript: direct link, hf CLI and curl.
- Browser
- Download file 423 Bytes
-
https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ibraheemmoosa/xlmindic-base-multiscript/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript/resolve/main/tokenizer_config.json
423 Bytes
| {"do_lower_case": false, "remove_space": true, "keep_accents": true, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "sp_model_kwargs": {}, "model_max_length": 512, "tokenizer_class": "AlbertTokenizer"} |