Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Krisna/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Krisna/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Krisna/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Krisna/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Krisna/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1a44d0d24f2bf2313c38dae3c6ccd27ba094c96206bd8ae19ae54e898f55f1e
- Size of remote file:
- 268 MB
- SHA256:
- 9578de5c69e7422680caadd39aa30953ea1de9d12108bc0bac3aa5a6a1db6eb2
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