Instructions to use deepset/bert-base-cased-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/bert-base-cased-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/bert-base-cased-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/bert-base-cased-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/bert-base-cased-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 508 Bytes
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"architectures": [
"BertForQuestionAnswering"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"language": "english",
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"name": "Bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 28996
}
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