Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use tester2047/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tester2047/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tester2047/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tester2047/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("tester2047/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f7499f15e7d6bc2a91546a59c07587f24e9797b7c30726e5100705009b4c7ec
- Size of remote file:
- 268 MB
- SHA256:
- 2683d1373a68229678c0aeb1b590f18b3157a4cfffac16eb765d66bb98dc81aa
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