Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JS21/finetuning-sentiment-model-10000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JS21/finetuning-sentiment-model-10000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JS21/finetuning-sentiment-model-10000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JS21/finetuning-sentiment-model-10000-samples") model = AutoModelForSequenceClassification.from_pretrained("JS21/finetuning-sentiment-model-10000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 135ddbb4a1ef3343cb122d333addfb77e3f03105a76af59f21ba439500a53c24
- Size of remote file:
- 268 MB
- SHA256:
- fe09b25fb7104f11ef1be1db7c69c787682ab167616f1d5adf620ce0de3c2db5
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