Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jmwolf27/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmwolf27/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jmwolf27/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jmwolf27/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("jmwolf27/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfa3c5fa85cfd9dab870de61a4919c4be7f7f0bc68b052cebb06ee1278ac04fb
- Size of remote file:
- 3.31 kB
- SHA256:
- cef09f6f92438e9656a480d6b7226a6f974d933a6c4d4ce2752c1ed1087a2fc4
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