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:
- a215adebd86a286e0bfaa5ffe86f8183aeb32826bcd65ef31ca331be4b1164dc
- Size of remote file:
- 268 MB
- SHA256:
- 5439a56ad11454a9636ddf13ef5aae4b30282d6517a71820ca1422cc41ef07ca
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