Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use lindeberg/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lindeberg/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lindeberg/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lindeberg/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("lindeberg/tiny-bert-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d7e89190a9e36cfe0e5a2e579150895c926524dfc536214e515d5bab7d8d362
- Size of remote file:
- 4.54 kB
- SHA256:
- f6cc123cf9dd4a78d4085405a2f923ed54aca911eac52085c45d99b4ae230f5b
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