Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JHart96/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JHart96/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JHart96/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JHart96/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("JHart96/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d94668f2124c35da3b032d856866738cb23625ea80d722b7b4b9294e68761434
- Size of remote file:
- 3.31 kB
- SHA256:
- 70557e19d22a89ea25fc3489e5279ea7010b68ddf60fefaec70938b5bc7654bc
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