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:
- b8d4e51790002ba8152adae35699a86ce827f865b23b96030d2e9ac8aacf04b7
- Size of remote file:
- 3.25 kB
- SHA256:
- b550bce2fe21715a50005b4bbb1c181a801412f6b49f51c6f10541b8e3a0f5f2
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