Instructions to use textattack/bert-base-uncased-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-snli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-snli") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.txt from textattack/bert-base-uncased-snli: direct link, hf CLI and curl.
- Browser
- Download file 113 Bytes
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https://huggingface.co/textattack/bert-base-uncased-snli/resolve/main/eval_results.txt
- Command line
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hf download hf://textattack/bert-base-uncased-snli/eval_results.txt
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curl -L -o eval_results.txt https://huggingface.co/textattack/bert-base-uncased-snli/resolve/main/eval_results.txt
113 Bytes
| eval_accuracy = 0.9048249185667753 | |
| eval_loss = 0.3065845295544168 | |
| global_step = 51504 | |
| loss = 0.17053774699018895 | |