Instructions to use djsull/aha-sent-similiar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djsull/aha-sent-similiar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="djsull/aha-sent-similiar")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("djsull/aha-sent-similiar") model = AutoModel.from_pretrained("djsull/aha-sent-similiar") - Notebooks
- Google Colab
- Kaggle
Update 1_Pooling/config.json
Browse files- 1_Pooling/config.json +1 -3
1_Pooling/config.json
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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}
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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