Instructions to use anlausch/aq_bert_ibm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anlausch/aq_bert_ibm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anlausch/aq_bert_ibm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anlausch/aq_bert_ibm") model = AutoModelForSequenceClassification.from_pretrained("anlausch/aq_bert_ibm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anlausch/aq_bert_ibm: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/anlausch/aq_bert_ibm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anlausch/aq_bert_ibm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anlausch/aq_bert_ibm/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9ce3bdd5ec65a0d642998ef68794b46a2c584e864bef78a20e27282bb15e07f4
- Size of remote file:
- 438 MB
- SHA256:
- 43c1dd6c650abe6dec3b3d35fa6d5ed46f70a0be63f2c3a3d5109524028df68c
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