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:
- c92187967b1b67d61ed1559f213d9e1be6ee594824d3895e99b263b9839dd64d
- Size of remote file:
- 268 MB
- SHA256:
- 70bba4025bdf77345d4c9a02bf96462c3929b3eade08004927622c8e31216a4e
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