Instructions to use SajjadAyoubi/distil-feature-as-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SajjadAyoubi/distil-feature-as-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SajjadAyoubi/distil-feature-as-text")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SajjadAyoubi/distil-feature-as-text") model = AutoModelForSequenceClassification.from_pretrained("SajjadAyoubi/distil-feature-as-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
base model: 'HooshvareLab/distilbert-fa-zwnj-base'
- trained for 2 epochs with 256 as max_length
- 3x faster than the Bert model despite having the same performance
model performance on test:
{'eval_loss': 0.3385954797267914,
'eval_roc_auc': 0.9378028883850424,
'eval_f1_score': 0.8662723907586265,
'eval_recall': 0.8818815783774419,
'eval_percision': 0.8512061541034324,
'eval_runtime': 204.8524,
'eval_samples_per_second': 229.609,
'eval_steps_per_second': 7.176,
'epoch': 2.0}
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