Instructions to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
Upload tiny models for DebertaV2ForSequenceClassification
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
- tf_model.h5 +2 -2
config.json
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"torch_dtype": "float32",
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"transformers_version": "4.28.0.dev0",
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"type_vocab_size": 16,
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"torch_dtype": "float32",
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"transformers_version": "4.28.0.dev0",
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"vocab_size": 128001
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pytorch_model.bin
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tf_model.h5
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size 16723696
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