Instructions to use Mathissimo/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathissimo/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mathissimo/bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mathissimo/bert_model") model = AutoModelForSequenceClassification.from_pretrained("Mathissimo/bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Mathissimo/bert_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Mathissimo/bert_model/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://Mathissimo/bert_model@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Mathissimo/bert_model/resolve/refs%2Fpr%2F1/model.safetensors
268 MB
- Xet hash:
- b003711110e625f9ed5ab263111cf550a3c820881a6be22cfc840f9741541bda
- Size of remote file:
- 268 MB
- SHA256:
- 461c3906563178b2c1277cb7a0fb78374c2869b39a97ca1cd0e91ca6fe49d5a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.