Instructions to use mbartolo/electra-large-synqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbartolo/electra-large-synqa with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="mbartolo/electra-large-synqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mbartolo/electra-large-synqa") model = AutoModelForQuestionAnswering.from_pretrained("mbartolo/electra-large-synqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 731 Bytes
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"_name_or_path": "google/electra-large-discriminator",
"architectures": [
"ElectraForQuestionAnswering"
],
"attention_probs_dropout_prob": 0.1,
"embedding_size": 1024,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "electra",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"summary_activation": "gelu",
"summary_last_dropout": 0.1,
"summary_type": "first",
"summary_use_proj": true,
"transformers_version": "4.5.0.dev0",
"type_vocab_size": 2,
"vocab_size": 30522
}
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