Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jacobduncan00/hackMIT-finetuned-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacobduncan00/hackMIT-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacobduncan00/hackMIT-finetuned-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacobduncan00/hackMIT-finetuned-sst2") model = AutoModelForSequenceClassification.from_pretrained("jacobduncan00/hackMIT-finetuned-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jacobduncan00/hackMIT-finetuned-sst2: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/jacobduncan00/hackMIT-finetuned-sst2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jacobduncan00/hackMIT-finetuned-sst2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jacobduncan00/hackMIT-finetuned-sst2/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 8888bf06d5252f107d8eee5925e44f1e5b6224b0f15879b4900ab7e4fcfc5513
- Size of remote file:
- 17.6 MB
- SHA256:
- 07cbe8bf1b94a7ea6d95d174b854676d1ddf5bc870a2d36d73622923ab611dfc
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