Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use PDM/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PDM/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PDM/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDM/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("PDM/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3c4ec341ce76c0c1ba81855b110edd3f413fa07d075a3595fdf4c8ac60c9f20
- Size of remote file:
- 268 MB
- SHA256:
- c51eb11857997bfdd546d6e445b8fefba4c1db7f6cf5f5effea964a15962d4b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.