Text Classification
Transformers
Safetensors
PyTorch
bert
sentiment-analysis
sst2
glue
text-embeddings-inference
Instructions to use mi55th/bert-sst2-nesterov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mi55th/bert-sst2-nesterov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mi55th/bert-sst2-nesterov")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mi55th/bert-sst2-nesterov") model = AutoModelForSequenceClassification.from_pretrained("mi55th/bert-sst2-nesterov", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mi55th/bert-sst2-nesterov: direct link, hf CLI and curl.
- Browser
- Download file 129 Bytes
-
https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/43c600ebd58989dee2567fda3217615da59ad93d/training_args.bin
- Command line
-
hf download hf://mi55th/bert-sst2-nesterov@43c600ebd58989dee2567fda3217615da59ad93d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/43c600ebd58989dee2567fda3217615da59ad93d/training_args.bin
129 Bytes
- Xet hash:
- f75447f4a2dff5f4271f0a5eaa943e0dae1416ec727d2a2283a0acce680cec0e
- Size of remote file:
- 129 Bytes
- SHA256:
- 422133b0b317abd9aa1cd1f3f64786d6e90834a80925e20e1055bd483ca6a595
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