Instructions to use liddlefish/privacyembeddingv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liddlefish/privacyembeddingv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liddlefish/privacyembeddingv2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liddlefish/privacyembeddingv2") model = AutoModel.from_pretrained("liddlefish/privacyembeddingv2") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.40.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 498649702
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