Instructions to use connectivity/feather_berts_86 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_86 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_86")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_86") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_86", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_86: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_86/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_86/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_86/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 4f47a7d298c126efde6fa1ccecce4af14a467d90a6e1dd86770281ddbd5e9c0c
- Size of remote file:
- 438 MB
- SHA256:
- 6408de7321fbc5afd1e316ef3ad4bd4409ab712b59b7c11fbab85ab1e9448581
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