Instructions to use nlpotato/roberta-base-e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpotato/roberta-base-e5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="nlpotato/roberta-base-e5")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nlpotato/roberta-base-e5") model = AutoModelForQuestionAnswering.from_pretrained("nlpotato/roberta-base-e5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 645 Bytes
40a9947 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"eos_token": "[SEP]",
"from_tf": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "saved_models/klue/roberta-base/LWJ_12-23-22-11/checkpoint-9500/",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/opt/ml/.cache/huggingface/hub/models--klue--roberta-base/snapshots/67dd433d36ebc66a42c9aaa85abcf8d2620e41d9/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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