Instructions to use hf-tiny-model-private/tiny-random-MvpForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MvpForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-MvpForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MvpForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MvpForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4766f9d2730cbc8527ed176c42cf317ae231794f665ec62d5ce5a153013d20b0
- Size of remote file:
- 120 kB
- SHA256:
- 131a076cf66eda8337d7857c791f4af993a677be61947c7817335853f755f15e
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