Instructions to use AmirErez/BBERT-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmirErez/BBERT-models with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AmirErez/BBERT-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix tokenizer_config.json: use PreTrainedTokenizerFast (bbert_768H_epoch100)
Browse files
bbert_768H_epoch100/tokenizer_config.json
CHANGED
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@@ -1,11 +1,8 @@
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{
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<cls>",
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"eos_token": "</s>",
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"is_local": true,
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"local_files_only": false,
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"mask_token": "<msk>",
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"max_length": 102,
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"model_max_length": 512,
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@@ -15,8 +12,8 @@
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"padding_side": "right",
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"sep_token": "<sep>",
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"stride": 0,
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"tokenizer_class": "
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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-
}
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{
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<cls>",
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"eos_token": "</s>",
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"mask_token": "<msk>",
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"max_length": 102,
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"model_max_length": 512,
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"padding_side": "right",
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"sep_token": "<sep>",
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"stride": 0,
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"tokenizer_class": "PreTrainedTokenizerFast",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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+
}
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