Transformers
PyTorch
Arabic
encoder-decoder
text2text-generation
AraBERT
BERT
BERT2BERT
MSA
Arabic Text Summarization
Arabic News Title Generation
Arabic Paraphrasing
Instructions to use malmarjeh/bert2bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malmarjeh/bert2bert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("malmarjeh/bert2bert") model = AutoModelForSeq2SeqLM.from_pretrained("malmarjeh/bert2bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from malmarjeh/bert2bert: direct link, hf CLI and curl.
- Browser
- Download file 381 Bytes
-
https://huggingface.co/malmarjeh/bert2bert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://malmarjeh/bert2bert/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/malmarjeh/bert2bert/resolve/main/tokenizer_config.json
381 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "do_basic_tokenize": true, "never_split": ["[بريد]", "[مستخدم]", "[رابط]"], "special_tokens_map_file": null, "name_or_path": "aubmindlab/bert-base-arabertv02"} |