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")# 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 model.safetensors from mi55th/bert-sst2-nesterov: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/main/model.safetensors
- Command line
-
hf download hf://mi55th/bert-sst2-nesterov/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/mi55th/bert-sst2-nesterov/resolve/main/model.safetensors
134 Bytes
- Xet hash:
- b272df4bad2c6828dea3cfe8e399a9ce08d728ceea85468ce21b0223a9b7cd1e
- Size of remote file:
- 134 Bytes
- SHA256:
- d3e9ce284f5729b5589281754306198b7d2e8f5f5640942641d1268827b1f3de
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