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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dfcfd5c114bc1dd709557d74c68448cda25fb8293fae5f3c680545e983a64b7
- Size of remote file:
- 366 kB
- SHA256:
- 1d0063b0ebe2b8edff5ad1a681072fc569001a17e6ddd5c701baaa9d1b49dea5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.