Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokuls/bert-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/bert-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/bert-base-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/bert-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("gokuls/bert-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gokuls/bert-base-sst2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/gokuls/bert-base-sst2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gokuls/bert-base-sst2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gokuls/bert-base-sst2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2617dc29620a4618cf0e3aa237cd424873cc4b8abbea1f3f1e52ea06edf42d02
- Size of remote file:
- 438 MB
- SHA256:
- 051d2413636a5636b135dc7caf4eaf50b0034964c780ac2ca33558a69f2f9dc1
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