Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use kmin06/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmin06/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kmin06/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kmin06/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("kmin06/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4398dbf06364c4783fc6e00c890b51dce088cb0476412c3315316f7e5ee1330
- Size of remote file:
- 3.96 kB
- SHA256:
- 9e355fcd66eb49009ce03c0f95ec5cc123e5fddc5d815b962d9a529cf6eabb45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.