Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use KoenBronstring/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoenBronstring/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KoenBronstring/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KoenBronstring/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("KoenBronstring/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 517f15b417a3b7c1859b4be7abefd0d93b87212c4582d7b8ad5b487149dfdcc4
- Size of remote file:
- 3.12 kB
- SHA256:
- 1dca88535d6ec4c274a7b2d5c25aed7d0adeca23d1f33ea39c936bec63292417
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