Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use tester2047/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tester2047/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tester2047/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tester2047/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("tester2047/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f90e916f97ab51dbd19e5cd3a7c2e82ef2ed39dcb150d8fc836705d28f8f18d
- Size of remote file:
- 3.38 kB
- SHA256:
- a72e76b4b92586b95d2d31c9c43e90f1fbf5f3b0ccb4f4ff60358ecbc62c7d7c
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