Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use kaisuke/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaisuke/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaisuke/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaisuke/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("kaisuke/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 762bf083a22e60f1842f60cc5298219a01c60472cbe8fbc348266f9f78ff779a
- Size of remote file:
- 3.31 kB
- SHA256:
- 0b385dcc537237a94b37afa6735dbca06a0ee72867131008a6859132665137f4
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