Instructions to use connectivity/feather_berts_49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_49")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_49") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_49", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download hans_eval_bert_49.json from connectivity/feather_berts_49: direct link, hf CLI and curl.
- Browser
- Download file 12.1 MB
-
https://huggingface.co/connectivity/feather_berts_49/resolve/main/hans_eval_bert_49.json
- Command line
-
hf download hf://connectivity/feather_berts_49/hans_eval_bert_49.json
-
curl -L -o hans_eval_bert_49.json https://huggingface.co/connectivity/feather_berts_49/resolve/main/hans_eval_bert_49.json
12.1 MB
- Xet hash:
- f0286d5be3fd691f6e8c58afe60a3d89cf2ebeb4b989eac74a1f606a3bd8df59
- Size of remote file:
- 12.1 MB
- SHA256:
- 22b381e9a4ed2ff02b1440599c9b0f475c15b631d360e328d959fe7bfa3ff42a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.