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:
- 440a9c93e3ff256278dfea2ef439522e706131f26138e1ffad5036ff1c203490
- Size of remote file:
- 3.12 kB
- SHA256:
- 190d0db615902e3c295834ce9ab70da829797fa5f6f725816c082f9f5f8d8189
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