Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Zaid/distilbert-base-uncased-finetuned-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zaid/distilbert-base-uncased-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zaid/distilbert-base-uncased-finetuned-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zaid/distilbert-base-uncased-finetuned-sst2") model = AutoModelForSequenceClassification.from_pretrained("Zaid/distilbert-base-uncased-finetuned-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9225706e54715ae3ca188d1e46ee9d9dfc6970a98d7ee1bf96c1c74d472372b1
- Size of remote file:
- 268 MB
- SHA256:
- 5b74546724c982b5f98e2053bedb8c649db012ae784b6b1ea45af4c7db58f986
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