Instructions to use hf-internal-testing/tiny-random-StableLmForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-StableLmForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-StableLmForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-StableLmForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-StableLmForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca3b5c6450e0c48cea6f8edb837604298a582875d05997b9dd0d6bf4e17cc72f
- Size of remote file:
- 456 kB
- SHA256:
- e8c18f57985eec582fe6b237e917d32913b697d59ee48a7f6e084640795e206e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.