Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use joebobby/finetuning-sentiment-model-5000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joebobby/finetuning-sentiment-model-5000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joebobby/finetuning-sentiment-model-5000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joebobby/finetuning-sentiment-model-5000-samples") model = AutoModelForSequenceClassification.from_pretrained("joebobby/finetuning-sentiment-model-5000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from joebobby/finetuning-sentiment-model-5000-samples: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/joebobby/finetuning-sentiment-model-5000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://joebobby/finetuning-sentiment-model-5000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/joebobby/finetuning-sentiment-model-5000-samples/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a2e3107aafd1195b862d36753b769b1b48179b7105ca5ecbcdc86020d715889d
- Size of remote file:
- 438 MB
- SHA256:
- d99f01464c34812f2d65fec514632a1d5ff3e8a344753373e259c3be71c8f41a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.