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:
- fc8acdcc9d6c1b9ef50512f4bde5dc2ce437e1fea5cca20d99c213d74fc837ae
- Size of remote file:
- 268 MB
- SHA256:
- 5dd8debc8fced7618baf994955437d135722b70e3cd6253606329e4fa0887f31
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