Instructions to use jacob-ml/reward-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacob-ml/reward-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacob-ml/reward-predictor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacob-ml/reward-predictor") model = AutoModelForSequenceClassification.from_pretrained("jacob-ml/reward-predictor") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- README.md +2 -2
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
README.md
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tags:
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- transformers
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pipeline_tag: text-classification
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model-index:
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- name: jacob-24b
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results: []
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language:
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---
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<p align="center">
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tags:
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- transformers
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pipeline_tag: text-classification
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language:
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- de
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model-index:
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- name: jacob-24b
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results: []
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---
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<p align="center">
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"is_local": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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