Instructions to use Palak/microsoft_deberta-large_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Palak/microsoft_deberta-large_squad 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="Palak/microsoft_deberta-large_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Palak/microsoft_deberta-large_squad") model = AutoModelForQuestionAnswering.from_pretrained("Palak/microsoft_deberta-large_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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datasets:
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- squad
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
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This model is a fine-tuned version of [microsoft_deberta-large_squad/checkpoint-14000](https://huggingface.co/microsoft_deberta-large_squad/checkpoint-14000) on the squad dataset.
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## Model description
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More information needed
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datasets:
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- squad
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model-index:
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- name: microsoft-deberta-large
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# microsoft-deberta-large
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This model is a fine-tuned version of [microsoft_deberta-large_squad/checkpoint-14000](https://huggingface.co/microsoft_deberta-large_squad/checkpoint-14000) on the squad dataset.
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- "eval_exact_match": 87.89025543992432
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- "eval_f1": 93.8755152147345
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- "eval_samples": 10788
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## Model description
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More information needed
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