Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Rami/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rami/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rami/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rami/results") model = AutoModelForSequenceClassification.from_pretrained("Rami/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rami/results: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Rami/results/resolve/0866c6d07d79ff0ba4cf2eefb5f7a0a0d6b7cc0a/pytorch_model.bin
- Command line
-
hf download hf://Rami/results@0866c6d07d79ff0ba4cf2eefb5f7a0a0d6b7cc0a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rami/results/resolve/0866c6d07d79ff0ba4cf2eefb5f7a0a0d6b7cc0a/pytorch_model.bin
268 MB
- Xet hash:
- 00d4178c8fb04a30e5429f358be245678df5b832ae5d254e47af5c7ef3be432d
- Size of remote file:
- 268 MB
- SHA256:
- 2168d77bdca7457d4269d67d377f5e5db9744e994a50de9a3541e333a5c5df7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.