Instructions to use connectivity/bert_ft_qqp-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/bert_ft_qqp-29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/bert_ft_qqp-29")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/bert_ft_qqp-29") model = AutoModelForSequenceClassification.from_pretrained("connectivity/bert_ft_qqp-29", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload qqp_eval@34110steps_bert_ft_qqp-29.json with git-lfs
d274db5 -
Upload paws_eval@34110steps_bert_ft_qqp-29.json
de3b11b -
Upload paws_eval@25000steps_bert_ft_qqp-29.json
067e8f4 -
Upload paws_eval@15000steps_bert_ft_qqp-29.json
1e089d2 -
Added training logs
2d63218 -
Saving weights and logs of step 34110
c6750c8 -
Saving weights and logs of step 30000
d2ddcdd -
Saving weights and logs of step 25000
9faa0fa -
Saving weights and logs of step 20000
aa34248 -
Saving weights and logs of step 15000
279ca6c -
Saving tokenizer
e28b312 -
initial commit
0806fa2