Sentence Similarity
Transformers
PyTorch
English
roberta
feature-extraction
Pytorch
Sentence Transformers
Transformers
Instructions to use digio/Twitter4SSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digio/Twitter4SSE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("digio/Twitter4SSE") model = AutoModel.from_pretrained("digio/Twitter4SSE") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- added_tokens.json +1 -0
- bpe.codes +0 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
added_tokens.json
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{"<mask>": 64000}
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bpe.codes
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": "<mask>"}
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tokenizer_config.json
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{"normalization": false, "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 128, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "my-vinai-bertweet-base_1000000_all_MultipleNegativesRankingLoss_new0/0_Transformer/"}
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vocab.txt
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