Instructions to use hf-internal-testing/tiny-random-FNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-FNetForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-FNetForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-FNetForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-FNetForSequenceClassification/resolve/f7b6e7d1cfa5e219277acf81b89b2b4dbfb4f1a0/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-FNetForSequenceClassification@f7b6e7d1cfa5e219277acf81b89b2b4dbfb4f1a0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-FNetForSequenceClassification/resolve/f7b6e7d1cfa5e219277acf81b89b2b4dbfb4f1a0/pytorch_model.bin
4.24 MB
- Xet hash:
- 28c2beead080c72c92dbd944ad59c58fecc867c43e3fc4972cf1d94f696553af
- Size of remote file:
- 4.24 MB
- SHA256:
- d8bbf422d7c2f8e4589e7b3113758935f3988532339ba2082ce65e6226abee35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.