Instructions to use TransWiC/bert-large-BT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-BT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-BT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-BT") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-BT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,340 Bytes
f71d41e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | Default classification report:
precision recall f1-score support
F 0.8787 0.8840 0.8814 500
T 0.8833 0.8780 0.8806 500
accuracy 0.8810 1000
macro avg 0.8810 0.8810 0.8810 1000
weighted avg 0.8810 0.8810 0.8810 1000
ADJ
Accuracy = 0.8888888888888888
Weighted Recall = 0.8888888888888888
Weighted Precision = 0.8888888888888888
Weighted F1 = 0.8888888888888888
Macro Recall = 0.8885448916408669
Macro Precision = 0.8885448916408669
Macro F1 = 0.8885448916408669
ADV
Accuracy = 0.7333333333333333
Weighted Recall = 0.7333333333333333
Weighted Precision = 0.7446428571428572
Weighted F1 = 0.7357466063348416
Macro Recall = 0.7361111111111112
Macro Precision = 0.7276785714285714
Macro F1 = 0.7285067873303168
NOUN
Accuracy = 0.8863636363636364
Weighted Recall = 0.8863636363636364
Weighted Precision = 0.8867057778822485
Weighted F1 = 0.8863310155131717
Macro Recall = 0.8863046129564531
Macro Precision = 0.8867485455720749
Macro F1 = 0.8863228603005555
VERB
Accuracy = 0.8825503355704698
Weighted Recall = 0.8825503355704698
Weighted Precision = 0.8827054794520548
Weighted F1 = 0.8825384312179739
Macro Recall = 0.8825503355704698
Macro Precision = 0.8827054794520548
Macro F1 = 0.882538431217974
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