Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-65")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-65") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-65", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Added training logs
96c29c2 -
Saving weights and logs of step 1603
77d798f -
Saving weights and logs of step 1600
a6d1fcf -
Saving weights and logs of step 1500
992f41b -
Saving weights and logs of step 1400
f52595c -
Saving weights and logs of step 1300
32b7e4c -
Saving weights and logs of step 1200
32933b4 -
Saving weights and logs of step 1100
8bd5b79 -
Saving weights and logs of step 1000
086439b -
Saving weights and logs of step 900
a07e38b -
Saving weights and logs of step 800
04f306e -
Saving weights and logs of step 700
2354ff5 -
Saving weights and logs of step 600
f16bed3 -
Saving weights and logs of step 500
5885105 -
Saving weights and logs of step 400
754cb34 -
Saving weights and logs of step 300
153b5aa -
Saving weights and logs of step 200
7bf05b2 -
Saving weights and logs of step 100
83c1b7f -
Saving weights and logs of step 0
4cda75d -
Saving tokenizer
a8ea06b -
initial commit
d4c584f