Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use dev-ninja/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dev-ninja/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dev-ninja/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dev-ninja/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("dev-ninja/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ded8bdce74f4b2b2abbbbcafb3ac21fa19c8a27bfc75401ca76f5c57fdc665ac
- Size of remote file:
- 4.03 kB
- SHA256:
- a25f1be3458f46de47337e51c7f8ba318d5962f79f3674a4c443ddbe8313525a
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