Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use zboxi7/finetuning-sentiment-model-1000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zboxi7/finetuning-sentiment-model-1000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zboxi7/finetuning-sentiment-model-1000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zboxi7/finetuning-sentiment-model-1000-samples") model = AutoModelForSequenceClassification.from_pretrained("zboxi7/finetuning-sentiment-model-1000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ce2d3de89a1a78a2bb18736a6168866d6fba81e57621bfc0cbd17217c8d60f2
- Size of remote file:
- 268 MB
- SHA256:
- 4a5ba4aa9225c0daeb564c81cd01777614c2e72e669789e221b2de088df86958
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.