Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rahuldebdas79/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahuldebdas79/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rahuldebdas79/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rahuldebdas79/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("rahuldebdas79/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dd03d6147549b8a5e366c6085852817c3095a45634bcabcb1826985e24aeeb4
- Size of remote file:
- 268 MB
- SHA256:
- b6bdf760bd1f4862a0c91f2db9a27bd27475ea7164434d65917891a9162be86a
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