Instructions to use hf-tiny-model-private/tiny-random-BertForSequenceClassification 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-BertForSequenceClassification 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-BertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-BertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0147ee81c7262363e4f0dc7144f193fee5ad9bf67231e462e7469fd005bfdd3c
- Size of remote file:
- 385 kB
- SHA256:
- 57856f2a5dc9d288c263b7be9cac12b64aba43e8874e2b40f7a966542adde92c
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