Instructions to use connectivity/feather_berts_29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_29")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_29") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_29", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_29: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_29/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_29/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_29/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 194097c38ac1d9169c13491dfbf5d3b91a7c4c29fd939ab009a567af362ff195
- Size of remote file:
- 438 MB
- SHA256:
- 4c90acd8def18678fcfc11ed675cfeb0c18c5f32d8ffca3c02ed6c05ebc722be
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