Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use federicopascual/finetuning-sentiment-analysis-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use federicopascual/finetuning-sentiment-analysis-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="federicopascual/finetuning-sentiment-analysis-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("federicopascual/finetuning-sentiment-analysis-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("federicopascual/finetuning-sentiment-analysis-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b74b38154d67ab4b8249884bf09e7ff3583b429f48c592b70cab0e943fa10811
- Size of remote file:
- 268 MB
- SHA256:
- 80a3ea74375831b520c1572a45cd59eb5536fd90fd8cad532a0f3e3a2cbfa770
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