Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-focus-victim 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-victim 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-victim")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-focus-victim") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-focus-victim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from responsibility-framing/predict-perception-bert-focus-victim: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/responsibility-framing/predict-perception-bert-focus-victim/resolve/main/training_args.bin
- Command line
-
hf download hf://responsibility-framing/predict-perception-bert-focus-victim/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/responsibility-framing/predict-perception-bert-focus-victim/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 29758fdfb47a09d48e081d01efd0022ade632dcdc7553854073f1f3281df8979
- Size of remote file:
- 3.12 kB
- SHA256:
- 737e8bc8d868a7272c7c2834f4be6676fc4945a9e10d049be83944df1268c23c
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