Text Classification
Transformers
PyTorch
Safetensors
bert
voicemail-detection
text-embeddings-inference
Instructions to use Adya662/bert-tiny-amd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adya662/bert-tiny-amd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adya662/bert-tiny-amd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adya662/bert-tiny-amd") model = AutoModelForSequenceClassification.from_pretrained("Adya662/bert-tiny-amd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8109c192fe6668bebabbe2df82edf5c0881d14d516ab8709756c5e4c0fcbbb1f
- Size of remote file:
- 17.5 MB
- SHA256:
- 7b6694c3a800b01632ff33191bd8a12d4e0d0376fc82f8c509743e7a55f94bec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.