Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ekiprop/SST-2-FULL_FT-seed52 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ekiprop/SST-2-FULL_FT-seed52 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ekiprop/SST-2-FULL_FT-seed52")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ekiprop/SST-2-FULL_FT-seed52") model = AutoModelForSequenceClassification.from_pretrained("ekiprop/SST-2-FULL_FT-seed52") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d23127a328679679ccc44d11238b92b6287fe63a807071abeb4bca7adbdda169
- Size of remote file:
- 5.37 kB
- SHA256:
- 70acbc097798307c2070e8c6215be0949d3eafd8f416ba4c3f506a5478b78cdd
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