Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use MRAIRR/7emotion_cls_in_context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRAIRR/7emotion_cls_in_context with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MRAIRR/7emotion_cls_in_context")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MRAIRR/7emotion_cls_in_context") model = AutoModelForSequenceClassification.from_pretrained("MRAIRR/7emotion_cls_in_context", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6412afd516e2f126adeb4c52dd105c127c70ea5e1adfa841b0af30bc2c979378
- Size of remote file:
- 4.98 kB
- SHA256:
- 900c6536ce9d9b43a272b30c4553b55599d0d1654e629b7ae7cf0d3127408451
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.