Instructions to use hf-tiny-model-private/tiny-random-LongformerForSequenceClassification 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-LongformerForSequenceClassification 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-LongformerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8688d68aab19cd1ab22fbf6f2487cc3c228f376b8fbead26c6910c4216f627c
- Size of remote file:
- 442 kB
- SHA256:
- cb59c9830e87b8d5e807820afcfa803be422a5ced51a7c6afd3cc364ac9c10ee
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