Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Abonia/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abonia/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abonia/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abonia/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Abonia/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc8a8cbdd885c58f1d81d0d69a59a63e84dcaf72f08295f8b5b8aa1e427e7df8
- Size of remote file:
- 268 MB
- SHA256:
- e33ff82672eca295c6166699ddc130e47e97f1a3a9e1c440c6fd2609110582f7
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