Instructions to use arpanghoshal/EkmanClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanghoshal/EkmanClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arpanghoshal/EkmanClassifier")# Load model directly from transformers import AutoTokenizer, BertForMultiLabelClassification tokenizer = AutoTokenizer.from_pretrained("arpanghoshal/EkmanClassifier") model = BertForMultiLabelClassification.from_pretrained("arpanghoshal/EkmanClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e26f7808a63012792f79b25f67d41e655e7279314d8c48f2723548cd2e3da21e
- Size of remote file:
- 433 MB
- SHA256:
- aa2a54d3263432abfde7f2fa93a76eb2b8d08de479d08c283fa961566b505d65
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.