Instructions to use harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain / similarity_evaluation_sts-test_results.csv
Download similarity_evaluation_sts-test_results.csv from harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
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https://huggingface.co/harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain/resolve/main/similarity_evaluation_sts-test_results.csv
- Command line
-
hf download hf://harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain/similarity_evaluation_sts-test_results.csv
-
curl -L -o similarity_evaluation_sts-test_results.csv https://huggingface.co/harish/AStitchInLanguageModels-Task2_PT_SentTransTokenizedNoPreTrain/resolve/main/similarity_evaluation_sts-test_results.csv
302 Bytes
| epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman | |
| -1,-1,0.7891897612333949,0.7762337375076835,0.7676382677738575,0.7631543888947879,0.7667147428273715,0.7626407907842212,0.6717872141005843,0.6116715833259426 | |