Instructions to use Cameron/BERT-eec-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-eec-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-eec-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-eec-emotion") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-eec-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cf444b2a92d3f9465f3a9d444f997251f26e86cba40644a61c8f90ac98b8140
- Size of remote file:
- 867 MB
- SHA256:
- d0c64cca0ffd2588d29574e229d94aef3ba9e4cba71949393a6e22c3c9c02a93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.