Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JS21/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JS21/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JS21/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JS21/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("JS21/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da399385b989b6dcbe47584ce0e3a4941748e7040558e603c9c23e14d853de0d
- Size of remote file:
- 3.52 kB
- SHA256:
- 4f125c193084d17fd3dfa34bb6dcc0fa2b0443c8271bc6821215c3049c849cd7
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