Instructions to use Andrum/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andrum/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Andrum/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Andrum/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Andrum/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Andrum/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/Andrum/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Andrum/finetuning-sentiment-model-3000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Andrum/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- b35588a1ed90f01d568dbd80eeff1d7a52902f1a045d4f8592dfe8372da1ac82
- Size of remote file:
- 439 MB
- SHA256:
- e398539ae3b2461d1ad2cc6a5608c3ba306519b8d78f587d9dd14e38ee326ff2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.