Instructions to use hf-internal-testing/tiny-random-BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-BertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-BertForSequenceClassification") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35600dc5e30565cb341306650908a8ef5ff11c957298eac830600a1f2893e103
- Size of remote file:
- 385 kB
- SHA256:
- 7e6e30f85edfbd82689b8863f8a0d30b696e0111c43636c6b2f98fa43ee818e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.