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:
- 278061c255ba258e8dbc729d75640e3e9fef3bf52a3b6d42c26694cea3138777
- Size of remote file:
- 268 MB
- SHA256:
- 2565e76728cedad8f937370adfd8263a5e9a73b4dd0ec65a0937b6b5d6ceaed8
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