Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use peringe/finetuning-sentiment-model-3000-samples-pi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peringe/finetuning-sentiment-model-3000-samples-pi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peringe/finetuning-sentiment-model-3000-samples-pi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("peringe/finetuning-sentiment-model-3000-samples-pi") model = AutoModelForSequenceClassification.from_pretrained("peringe/finetuning-sentiment-model-3000-samples-pi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adb5f187bf2e7b236296bf7fb679fbe3b096d83d4d9f00b6702f86913525edd7
- Size of remote file:
- 3.12 kB
- SHA256:
- 4743a21f5b3443de04b16866b4e96b4e9b9b4e41deb213cf13974ff97c89a3f1
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