Instructions to use hf-tiny-model-private/tiny-random-RemBertForSequenceClassification 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-RemBertForSequenceClassification 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-RemBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d15731fbeb30ef30855039a9ac78c7e4cf46e241985250de3b1c87d7a0945a6
- Size of remote file:
- 18.2 MB
- SHA256:
- fa734b70325358eb95e6fb61c928fe71d68697b3ce828ba3810635f1913349f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.