Instructions to use ba-claim/bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ba-claim/bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ba-claim/bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ba-claim/bert") model = AutoModelForSequenceClassification.from_pretrained("ba-claim/bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ba-claim/bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ba-claim/bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ba-claim/bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ba-claim/bert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d72ff57a370d4a02d3bcabced36c624f15e12dd087238a3a92e4accf19fb539f
- Size of remote file:
- 438 MB
- SHA256:
- 454d4ee910438e250e0c0c5ae05c878cd763d96d7d08837712d471e126b9f6fc
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