Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-focus-object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use responsibility-framing/predict-perception-bert-focus-object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="responsibility-framing/predict-perception-bert-focus-object")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-focus-object") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-focus-object", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from responsibility-framing/predict-perception-bert-focus-object: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/responsibility-framing/predict-perception-bert-focus-object/resolve/main/training_args.bin
- Command line
-
hf download hf://responsibility-framing/predict-perception-bert-focus-object/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/responsibility-framing/predict-perception-bert-focus-object/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 1417af80ef16a7ba8322a575c8eb1d7d6c179512b8b8aab088451f1a760a8dd6
- Size of remote file:
- 3.12 kB
- SHA256:
- 9802e39c62e8769d0cb465120eead0e4f1c780ccd8455d447224b630b12742e5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.