Question Answering
Transformers
PyTorch
TensorFlow
Safetensors
PEFT
English
deberta-v2
deberta
deberta-v3
squad
squad_v2
lora
Eval Results (legacy)
Instructions to use sjrhuschlee/deberta-v3-large-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjrhuschlee/deberta-v3-large-squad2 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="sjrhuschlee/deberta-v3-large-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sjrhuschlee/deberta-v3-large-squad2") model = AutoModelForQuestionAnswering.from_pretrained("sjrhuschlee/deberta-v3-large-squad2", device_map="auto") - PEFT
How to use sjrhuschlee/deberta-v3-large-squad2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Commit 路
4d636af
1
Parent(s): e517659
Upload adapter_config.json with huggingface_hub
Browse files- adapter_config.json +19 -0
adapter_config.json
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{
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"base_model_name_or_path": "microsoft/deberta-v3-large",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"modules_to_save": ["qa_outputs"],
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"peft_type": "LORA",
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"r": 8,
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"target_modules": [
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"query_proj",
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"key_proj",
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"value_proj",
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"dense"
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],
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"task_type": "QUESTION_ANS"
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}
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