Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Sayantan2001/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayantan2001/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayantan2001/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayantan2001/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Sayantan2001/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 424e7839ef6ba5c3ab199533252f58115d3db683384501d59406cbaa78357344
- Size of remote file:
- 268 MB
- SHA256:
- a87b41366edebc13845c7ca44b768a033ffa89250c9dccc88a21d56d17f5e0a3
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