Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rithwik-db/finetuning-sentiment-model-3000-samples-4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") model = AutoModelForSequenceClassification.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a697661426d1aef079178ad6b2cf812013e4e0ba36aff73e7fb59f7d215a6be9
- Size of remote file:
- 268 MB
- SHA256:
- 03d6340091cdb394d34a3c5265b389cbbbef85d22c8c2e39370524efbd19a0ee
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