Instructions to use connectivity/feather_berts_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_7") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download hans_eval_bert_7.json from connectivity/feather_berts_7: direct link, hf CLI and curl.
- Browser
- Download file 12.1 MB
-
https://huggingface.co/connectivity/feather_berts_7/resolve/main/hans_eval_bert_7.json
- Command line
-
hf download hf://connectivity/feather_berts_7/hans_eval_bert_7.json
-
curl -L -o hans_eval_bert_7.json https://huggingface.co/connectivity/feather_berts_7/resolve/main/hans_eval_bert_7.json
12.1 MB
- Xet hash:
- f8600808285312796298a313ff05bcaf236f24d68834ae40ebd9b6b8bc493a56
- Size of remote file:
- 12.1 MB
- SHA256:
- cd69bc85c2cebf3401bb14613f0bbd9f9ca2e1eb1c2328f032faee0f0daf7141
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