Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Zul/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zul/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zul/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zul/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Zul/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4509c907796c2676a0a2d2a2af4a58591a96fd449f09c93fb3cd7decf7a5d3f9
- Size of remote file:
- 3.38 kB
- SHA256:
- a9e63c9a5af9dc97944b7e6426c3908822581f3ab9a693f0dc28c42372931428
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