Instructions to use tner/bert-large-tweetner7-continuous with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/bert-large-tweetner7-continuous with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/bert-large-tweetner7-continuous")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/bert-large-tweetner7-continuous") model = AutoModelForTokenClassification.from_pretrained("tner/bert-large-tweetner7-continuous", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add tokenizer
Browse files- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"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, "model_max_length": 512, "name_or_path": "cner_output/model/baseline_2021/bert_large_continuous/best_model", "special_tokens_map_file": "/home/c.c2042013/.cache/huggingface/transformers/dedfc9f0e4ac921a476db22ccb98cf583956e8496609dd41a02a6c13a4221c7a.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "tokenizer_class": "BertTokenizer"}
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vocab.txt
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