Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ocm/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ocm/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ocm/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ocm/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("ocm/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f4cf8f3ee5f9134fc9eaf83cbc420f1ffec30689a2735c3dcab103be891fb36
- Size of remote file:
- 268 MB
- SHA256:
- 1a948a748232d0aa9217e76a251058164f8e808c0a6bf6f3283bf0188d5fdac0
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