Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Philip-Jan/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Philip-Jan/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Philip-Jan/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Philip-Jan/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Philip-Jan/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfbd8736af9e04dc138cab9bd7ed2ed0b7c09b9c8da4d99c75ab49dad81f56ce
- Size of remote file:
- 3.12 kB
- SHA256:
- 636e08246031652885430d9d298f9b8f7c0de46d1ea2ddecf4aa5d9ef9a84650
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