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:
- 8b651b9c5192f284e3a0d0f1009d73e4d48250519b6f35ab574d914a338a3f86
- Size of remote file:
- 268 MB
- SHA256:
- 1aabdc5546589bdf6eb80aa691023ca8117f5322b1d824345cac07c4280da925
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