Instructions to use connectivity/bert_ft_qqp-26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-26")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-26") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-26", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-26.json with git-lfs
d67695e -
Upload paws_eval@34110steps_bert_ft_qqp-26.json
24ce105 -
Upload paws_eval@25000steps_bert_ft_qqp-26.json
de4e1f9 -
Upload paws_eval@15000steps_bert_ft_qqp-26.json
dffad67 -
Added training logs
95451c1 -
Saving weights and logs of step 34110
91babe8 -
Saving weights and logs of step 30000
1f87964 -
Saving weights and logs of step 25000
272aa13 -
Saving weights and logs of step 20000
306c38d -
Saving weights and logs of step 15000
f0e0561 -
Saving tokenizer
9720619 -
initial commit
4baecb4