Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-focus-concept 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-concept 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-concept")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-focus-concept") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-focus-concept", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from responsibility-framing/predict-perception-bert-focus-concept: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/responsibility-framing/predict-perception-bert-focus-concept/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://responsibility-framing/predict-perception-bert-focus-concept/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/responsibility-framing/predict-perception-bert-focus-concept/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 0007e6b9215a935c932ef6f9511ed646a2efaba30bd559baab9052812a5eb734
- Size of remote file:
- 443 MB
- SHA256:
- 2cd38eb3a03e5e9e02c2ceb8c0da8c24aba891c7626d5a3519502290a7adbb07
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