Instructions to use beva/beavers-dam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use beva/beavers-dam with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("beva/beavers-dam", set_active=True) - Notebooks
- Google Colab
- Kaggle
Upload 2 files
Browse files- config.json +2 -1
- generation_config.json +5 -0
config.json
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{
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"_name_or_path": "avichr/heBERT_sentiment_analysis",
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"architectures": [
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"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"total_flos": 6997313242916978688,
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"transformers_version": "4.28.1",
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"type_vocab_size": 2,
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{
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"_name_or_path": "avichr/heBERT_sentiment_analysis",
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"total_flos": 6997313242916978688,
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"transformers_version": "4.28.1",
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"type_vocab_size": 2,
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generation_config.json
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{
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"_from_model_config": true,
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"pad_token_id": 0,
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"transformers_version": "4.28.1"
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}
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