Instructions to use Crasher222/kaggle-comp-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crasher222/kaggle-comp-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Crasher222/kaggle-comp-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test") model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"tags" must be an array
Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test")
tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test")
inputs = tokenizer("I am in love with you", return_tensors="pt")
outputs = model(**inputs)
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