Instructions to use akum1343/Classification_model4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akum1343/Classification_model4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akum1343/Classification_model4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akum1343/Classification_model4") model = AutoModelForSequenceClassification.from_pretrained("akum1343/Classification_model4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from akum1343/Classification_model4: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/akum1343/Classification_model4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://akum1343/Classification_model4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/akum1343/Classification_model4/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 6c71f7b4eab3a43feedbf08f9e19efd8bb78fd983d37609cc1d992a266daea36
- Size of remote file:
- 268 MB
- SHA256:
- 867e1e6cd85464348b0f61a37f2ceaa5f53318cb65684f56592ade44ea42f2da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.