Text Classification
PEFT
Safetensors
Arabic
French
darija
arabic
moroccan-arabic
arabizi
nlp
lora
bertouch
Instructions to use MedAdil/BERTal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MedAdil/BERTal with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("AbderrahmanSkiredj1/BERTouch") model = PeftModel.from_pretrained(base_model, "MedAdil/BERTal") - Notebooks
- Google Colab
- Kaggle
File size: 702 Bytes
e9e4799 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"is_local": false,
"mask_token": "[MASK]",
"max_len": 512,
"max_length": 128,
"model_max_length": 512,
"never_split": [
"[بريد]",
"[مستخدم]",
"[رابط]"
],
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]",
"use_fast": true
}
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