Instructions to use opticalmaterials/opticalpurebert_abstract_classification_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opticalmaterials/opticalpurebert_abstract_classification_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="opticalmaterials/opticalpurebert_abstract_classification_uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("opticalmaterials/opticalpurebert_abstract_classification_uncased") model = AutoModelForSequenceClassification.from_pretrained("opticalmaterials/opticalpurebert_abstract_classification_uncased") - Notebooks
- Google Colab
- Kaggle
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Parent(s): 738e0c4
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tokenizer_config.json
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{"do_lower_case": true, "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, "max_len": 512, "use_fast": true, "special_tokens_map_file": null, "name_or_path": "/projects/SolarWindowsADSP/jiuyang/opticalbert/pretrain/opticalbertpure_uncased/output/", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
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