Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Saidur49/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saidur49/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Saidur49/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Saidur49/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("Saidur49/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Saidur49/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Saidur49/my_awesome_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Saidur49/my_awesome_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Saidur49/my_awesome_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 425803b4448f803709478138fc4b5c71f142f2e638fd6aa8ec4c914b1127dc35
- Size of remote file:
- 268 MB
- SHA256:
- d632a433b32686372d8eaecfdf1af3d0e0540f0e20390bae9c82307811304886
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.