Instructions to use connectivity/feather_berts_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_30")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_30") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from connectivity/feather_berts_30: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/connectivity/feather_berts_30/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://connectivity/feather_berts_30/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/connectivity/feather_berts_30/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 4d4e3e264b12d2c1d95a40b0ba28f5143128fc5dfe82a49e4d04e64fbb7e3dec
- Size of remote file:
- 438 MB
- SHA256:
- cd32a81de8d85d0802ef29e414d059c1e0231a78cfdd1069b4d04f65cb7f5279
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