Text Classification
Transformers
PyTorch
TensorFlow
JAX
albert
multilingual
xlmindic
nlp
indoaryan
indicnlp
iso15919
transliteration
Instructions to use ibraheemmoosa/xlmindic-base-uniscript-soham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-uniscript-soham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ibraheemmoosa/xlmindic-base-uniscript-soham")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript-soham") model = AutoModelForSequenceClassification.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript-soham", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"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"} |