Instructions to use Chris-S87/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chris-S87/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chris-S87/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chris-S87/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Chris-S87/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8bb1e5eae7f00adabd63910d569363ab43ecfe3c08a85f8334cc574beb5bd98
- Size of remote file:
- 2.99 kB
- SHA256:
- 1615807dcbd6e43d414de55b170c7ac885cfeaf8c109e33dedd64e8b96d15351
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