Instructions to use xeventminer/mbert-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xeventminer/mbert-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xeventminer/mbert-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xeventminer/mbert-es") model = AutoModelForSequenceClassification.from_pretrained("xeventminer/mbert-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from xeventminer/mbert-es: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/xeventminer/mbert-es/resolve/main/optimizer.pt
- Command line
-
hf download hf://xeventminer/mbert-es/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/xeventminer/mbert-es/resolve/main/optimizer.pt
1.42 GB
- Xet hash:
- 9f13f0321efc90e22b0705f070f0c3546716e1c110291f568040b373069d7744
- Size of remote file:
- 1.42 GB
- SHA256:
- b499358c51d7922cdd2b163d58773134d35959a5fb5a92a93a3d1cf20ddb2628
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.