Instructions to use EricPeter/distilbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/distilbert-base-cased 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="EricPeter/distilbert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("EricPeter/distilbert-base-cased") model = AutoModelForQuestionAnswering.from_pretrained("EricPeter/distilbert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 34
Browse files- README.md +4 -2
- tf_model.h5 +1 -1
README.md
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Epoch:
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## Model description
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### Framework versions
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.3454
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- Epoch: 34
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## Model description
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### Framework versions
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tf_model.h5
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